Machine Learning in R
Tim Hoolihan (@thoolihan)
2018.05.04
Tim Hoolihan
- Organizer of the Cleveland R User Group
- Sr. Director of Data Science at DialogTech
- Advisory Board Member at DriveIT in Akron
- Author of a Packt Pub Video Series on Machine Learning in R
- @thoolihan on github & twitter
We are in #2018–purple
About the Talk
- Super-brief crash course in R
- What is machine learning
- Regression - Predicting Continuous Values
- Classification - Predicting Discrete(Categorical) Values
- Briefly touch on Deep Learning
- Your Next Steps
R
- An implementation of the S language, that has evolved on it's own
- Built-in support for Statistics
- Lots of packages for a variety of STEM domains
- Considered a large reason for the growth of the R Community
- Integrations with a lot of systems (Most RDBMSs, Hadoop, Spark, ElasticSearch, etc)
- r-project.org
Unique Things About R Syntax
Assignment supports =, but <- is dominant in practice
x = 5
# above is the same as below
x <- 5
Unique Things About R Syntax
Functions declared through assignment, implied return
foo <- function(x, y) {
return(x + y)
}
# above is the same as below
foo <- function(x, y) {
x + y
}
Vectors and Vector Operations
heights <- c(1.7, 1.72, 1.68, 1.6, 1.8)
length(heights)
[1] 5
mean(heights)
[1] 1.7
sd(heights)
[1] 0.07211103
Vectors and Vector Operations
heights <- c(1.7, 1.72, 1.68, 1.6, 1.8)
feet_per_meter <- 3.28
tmp <- heights * feet_per_meter
# above is preferred and more performant than
tmp <- c()
for(height in heights){
tmp <- c(tmp, height * feet_per_meter)
}
Distributions
# normal distribution
heights = rnorm(500, mean = 66, sd = 3.5)
heights[1:5]
[1] 60.62870 73.56895 65.95143 67.09202 63.73548
Histogram
hist(heights, breaks = 12)
Let's make a related variable with some noise
# uniform distrubution added to heights
armspans <- heights + runif(500, min = -3, max = 3)
armspans[1:5]
[1] 60.87137 71.33070 68.47055 69.01529 61.62902
Data Frames
people <- data.frame(height = heights, armspan = armspans)
head(people)
height armspan
1 60.62870 60.87137
2 73.56895 71.33070
3 65.95143 68.47055
4 67.09202 69.01529
5 63.73548 61.62902
6 65.77522 67.41507
Data Frames
The dim function (dimensions) shows 500 observations of 2 variables
dim(people)
[1] 500 2
Indexing Data Frame
Here's how we examine a column
people$height
[1] 60.62870 73.56895 65.95143 67.09202 63.73548 65.77522 66.38611
[8] 60.03454 63.09837 64.64022 67.35349 63.67888 64.57144 66.18271
[15] 64.48197 68.33149 68.95761 62.87885 71.63755 66.31921 61.32802
[22] 63.17403 61.24231 65.21418 73.47149 69.75793 66.11212 63.71224
[29] 64.13810 66.16100 65.27679 70.31386 64.35038 68.00411 67.32321
[36] 71.01687 65.18402 67.95023 62.03783 67.26596 68.03087 61.40453
[43] 61.92934 67.49580 66.67926 69.43714 68.28942 67.23306 61.17816
[50] 64.31405 64.40716 65.93094 66.36876 70.53179 62.51292 68.04629
[57] 63.62123 67.81403 60.96798 59.94048 66.18714 64.71973 68.08964
[64] 64.42948 70.83155 68.01862 68.62457 64.92248 68.92371 65.72029
[71] 65.12229 65.50999 61.95921 68.86296 68.19836 65.27526 62.99073
[78] 72.81346 59.75806 65.18434 73.78684 64.67633 73.88640 65.76192
[85] 68.70547 63.80814 73.46139 66.64308 70.97057 62.60147 64.40761
[92] 63.31632 62.67067 63.51691 66.94662 68.40541 69.94629 58.05241
[99] 69.59998 65.69709 65.12179 69.47981 68.24155 60.27286 60.46947
[106] 70.48652 67.39169 68.17440 69.60405 67.51312 62.63882 56.10957
[113] 62.75392 63.73545 65.02181 77.27485 62.38748 63.10171 67.31369
[120] 62.79321 73.02578 64.59256 65.63077 67.99315 65.52305 59.82668
[127] 71.92276 64.11959 65.31827 68.23354 63.66959 67.18444 66.87658
[134] 70.89969 65.39296 65.07793 63.58700 72.26444 66.60413 59.71390
[141] 65.26257 64.57844 64.45371 71.35544 68.06994 64.74630 63.69623
[148] 65.74555 69.75574 68.25864 62.50905 62.73677 66.81269 61.97506
[155] 68.66917 65.41201 69.95726 70.91214 69.12110 66.05212 73.06126
[162] 64.93482 65.73257 59.93916 67.58264 68.19643 67.02359 72.01009
[169] 65.09119 66.50834 63.83636 70.93393 66.05243 73.27740 61.52315
[176] 62.82668 68.43167 65.14653 61.37096 71.54715 68.44545 67.21152
[183] 66.08876 64.47169 65.51361 66.43280 69.74323 66.67103 70.19243
[190] 68.43197 62.25435 75.01762 62.25037 70.69740 62.14842 66.82448
[197] 67.24732 70.04974 67.59441 65.53377 61.12143 70.66022 65.79065
[204] 62.68359 64.92492 60.00128 78.10018 68.01728 68.82824 66.89575
[211] 71.27420 71.00045 64.58460 71.95882 74.56729 72.69288 68.01344
[218] 64.78008 68.05000 63.41676 67.45624 66.89028 66.13992 61.64565
[225] 72.78303 63.06280 62.54173 60.52306 64.79181 73.09336 67.89819
[232] 72.58878 60.94478 59.63943 61.67703 69.05040 62.76401 61.73795
[239] 69.62148 68.28080 63.52788 68.46964 68.78898 65.05764 68.35597
[246] 70.58196 69.07163 66.57263 62.17133 69.05888 70.03955 69.81680
[253] 62.52447 67.22600 74.30514 64.51073 68.24466 62.78329 66.14063
[260] 70.86651 72.29287 63.61936 67.86107 65.59159 68.03930 71.50956
[267] 59.46389 66.74784 65.41965 66.05451 67.15774 71.06698 66.36099
[274] 60.70044 64.55880 62.22943 62.84438 65.22406 60.12442 61.31367
[281] 65.95675 64.40532 65.32963 66.29778 73.66363 65.26409 68.17779
[288] 76.08612 57.12575 63.64665 69.07726 65.57866 68.19108 65.22301
[295] 65.08067 64.87229 67.95642 64.35121 69.63280 67.91065 70.47380
[302] 62.47596 66.03875 66.15167 59.76509 68.76288 69.76901 65.47378
[309] 66.31679 67.01242 66.61873 65.41921 65.94592 62.79955 60.25100
[316] 70.93250 65.57985 68.14299 70.98151 64.70934 66.93683 76.55776
[323] 65.98704 67.40567 71.49025 65.35095 67.25122 63.74499 74.18365
[330] 70.76935 72.05925 71.80645 65.30472 69.23811 61.53564 70.50125
[337] 68.86354 68.46742 67.52113 70.33257 60.82981 64.85267 65.29563
[344] 62.98867 73.56694 67.19767 65.17900 68.18808 72.92542 66.79593
[351] 60.60274 63.84026 67.68119 63.43628 68.95476 67.06900 67.63089
[358] 67.57084 69.40879 64.86222 67.74107 63.92308 68.58149 64.94003
[365] 64.70776 69.90590 60.88474 63.89577 62.99582 69.55412 68.33736
[372] 64.22386 60.79042 70.47499 66.11643 67.32704 65.87842 66.01970
[379] 62.45457 65.70133 72.30919 68.98563 60.34540 65.98672 62.45777
[386] 63.67251 67.57613 61.71024 65.37755 62.82172 66.25270 71.74910
[393] 69.93682 62.31402 66.29842 68.42810 61.95063 67.19873 71.29669
[400] 70.17617 64.81337 69.14547 65.46255 65.06654 63.14364 65.12586
[407] 67.23247 67.62110 70.02647 69.30029 72.99750 64.94413 62.10118
[414] 69.52684 71.15921 65.57724 65.32443 69.20753 66.71795 68.25052
[421] 66.06511 61.21388 69.99306 67.33811 66.58575 60.92931 61.71373
[428] 63.70836 67.31115 61.65879 71.12888 66.54122 61.76261 68.34315
[435] 65.58440 64.29985 65.62579 64.97857 65.74737 62.71308 69.45046
[442] 68.98801 61.94261 62.68882 61.05315 71.76333 66.38864 64.65740
[449] 68.37692 67.62022 69.49350 62.02332 58.07529 68.16860 65.57613
[456] 69.16065 63.76305 65.38228 67.70117 62.74346 64.36608 70.83828
[463] 67.16180 69.31872 74.54827 66.80545 69.03882 67.34174 66.68564
[470] 60.82888 66.19340 69.82842 62.32559 63.98741 66.56513 69.04712
[477] 71.36017 65.87823 62.59562 68.78528 60.76952 61.37059 69.48542
[484] 68.08233 74.16197 64.49671 61.14482 66.12192 65.51637 64.41974
[491] 65.20649 71.36714 66.40974 66.24808 65.58922 64.00654 61.80511
[498] 62.51984 66.06400 66.57682
Indexing Data Frame
Here's how we examine a row
people[2,]
height armspan
2 73.56895 71.3307
Indexing Data Frame
Here's how we index both a row and column
people[2, 'height']
[1] 73.56895
Libraries
library(ggplot2)
Plot Data
qplot(x = height, y = armspan, data = people)
Syntax Convention
Notice that we didn't say people$height in the x parameter…
qplot(x = height, y = armspan, data = people)
in most languages you would need to do something like this:
with(people,
{qplot(x = height, y = armspan)}
)
A lot of functions take a data argument as the environment in which the evaluate the arguments
Formula
armspan ~ heightis a formula, can be read as “armspan by height”
reg_form <- lm(armspan ~ height, data = people)
Summary of our Regression Line
summary(reg_form)
Call:
lm(formula = armspan ~ height, data = people)
Residuals:
Min 1Q Median 3Q Max
-3.08956 -1.56482 0.07192 1.57984 3.08450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.48462 1.47120 1.009 0.313
height 0.97749 0.02213 44.179 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.76 on 498 degrees of freedom
Multiple R-squared: 0.7967, Adjusted R-squared: 0.7963
F-statistic: 1952 on 1 and 498 DF, p-value: < 2.2e-16
Regression
- lm is a function to fit a linear model
reg_form$coefficients
(Intercept) height
1.4846226 0.9774937
- Remember y = mx + b
- armspan = 0.9774937 * height + 1.4846226
Apply Formula
people$rline <- predict(reg_form, newdata = people)
people$rline
[1] 60.74879 73.39781 65.95173 67.06665 63.78566 65.77949 66.37662
[8] 60.16801 63.16288 64.67004 67.32224 63.73033 64.60280 66.17781
[15] 64.51535 68.27823 68.89025 62.94831 71.50988 66.31124 61.43237
[22] 63.23684 61.34860 65.23108 73.30254 69.67256 66.10880 63.76293
[29] 64.17921 66.15659 65.29228 70.21598 64.38671 67.95822 67.29264
[36] 70.90317 65.20160 67.90555 62.12622 67.23668 67.98437 61.50717
[43] 62.02017 67.46134 66.66319 69.35899 68.23710 67.20451 61.28589
[50] 64.35121 64.44222 65.93171 66.35967 70.42900 62.59061 67.99945
[57] 63.67398 67.77242 61.08044 60.07607 66.18213 64.74776 68.04182
[64] 64.46403 70.72202 67.97240 68.56471 64.94594 68.85712 65.72580
[71] 65.14125 65.52023 62.04936 68.79773 68.14810 65.29078 63.05766
[78] 72.65932 59.89775 65.20191 73.61080 64.70533 73.70812 65.76649
[85] 68.64379 63.85668 73.29267 66.62782 70.85791 62.67717 64.44266
[92] 63.37593 62.74481 63.57200 66.92453 68.35048 69.85668 58.23049
[99] 69.51817 65.70312 65.14077 69.40070 68.19031 60.40096 60.59315
[106] 70.38475 67.35958 68.12467 69.52214 67.47827 62.71367 56.33137
[113] 62.82618 63.78562 65.04304 77.02031 62.46800 63.16615 67.28333
[120] 62.86459 72.86686 64.62344 65.63829 67.94750 65.53299 59.96483
[127] 71.78867 64.16112 65.33282 68.18248 63.72124 67.15699 66.85606
[134] 70.78862 65.40583 65.09789 63.64051 72.12266 66.58974 59.85459
[141] 65.27838 64.60964 64.48772 71.23412 68.02256 64.77373 63.74729
[148] 65.75048 69.67043 68.20702 62.58682 62.80942 66.79361 62.06486
[155] 68.60831 65.42446 69.86741 70.80080 69.05007 66.05016 72.90155
[162] 64.95800 65.73780 60.07477 67.54623 68.14621 66.99976 71.87404
[169] 65.11085 66.49610 63.88426 70.82209 66.05046 73.11283 61.62312
[176] 62.89731 68.37615 65.16495 61.47435 71.42152 68.38962 67.18346
[183] 66.08597 64.50529 65.52376 66.42226 69.65819 66.65513 70.09729
[190] 68.37645 62.33786 74.81388 62.33396 70.59089 62.23431 66.80513
[197] 67.21845 69.95780 67.55773 65.54347 61.23044 70.55455 65.79457
[204] 62.75744 64.94833 60.13550 77.82706 67.97108 68.76380 66.87480
[211] 71.15470 70.88712 64.61567 71.82391 74.37368 72.54146 67.96734
[218] 64.80674 68.00307 63.47411 67.42267 66.86945 66.13598 61.74286
[225] 72.62958 63.12811 62.61877 60.64553 64.81821 72.93293 67.85468
[232] 72.43970 61.05776 59.78179 61.77354 68.98096 62.83605 61.83308
[239] 69.53918 68.22867 63.58273 68.41327 68.72542 65.07806 68.30215
[246] 70.47805 69.00171 66.55895 62.25671 68.98924 69.94785 69.73011
[253] 62.60190 67.19762 74.11743 64.54346 68.19335 62.85489 66.13667
[260] 70.75619 72.15045 63.67215 67.81839 65.59999 67.99261 71.38477
[267] 59.61021 66.73022 65.43192 66.05249 67.13090 70.95215 66.35207
[274] 60.81893 64.59045 62.31350 62.91461 65.24073 60.25587 61.41835
[281] 65.95693 64.44041 65.34392 66.29029 73.49036 65.27986 68.12799
[288] 75.85833 57.32469 63.69883 69.00721 65.58735 68.14098 65.23970
[295] 65.10057 64.89688 67.91160 64.38753 69.55025 67.86686 70.37232
[302] 62.55448 66.03708 66.14746 59.90462 68.69991 69.68340 65.48483
[309] 66.30887 66.98885 66.60401 65.43149 65.94634 62.87078 60.37960
[316] 70.82070 65.58852 68.09397 70.86860 64.73760 66.91496 76.31935
[323] 65.98654 67.37325 71.36590 65.36477 67.22227 63.79495 73.99867
[330] 70.66122 71.92208 71.67498 65.31958 69.16444 61.63532 70.39916
[337] 68.79830 68.41109 67.48611 70.23427 60.94538 64.87770 65.31069
[344] 63.05566 73.39585 67.16993 65.19669 68.13804 72.76876 66.77723
[351] 60.72342 63.88808 67.64257 63.49319 68.88747 67.04415 67.59339
[358] 67.53470 69.33128 64.88703 67.70110 63.96903 68.52260 64.96310
[365] 64.73606 69.81720 60.99908 63.94234 63.06264 69.47334 68.28396
[372] 64.26304 60.90688 70.37349 66.11302 67.29638 65.88036 66.01847
[379] 62.53357 65.70726 72.16640 68.91764 60.47187 65.98623 62.53670
[386] 63.72410 67.53987 61.80600 65.39077 62.89246 66.24622 71.61892
[393] 69.84743 62.39619 66.29092 68.37266 62.04098 67.17096 71.17669
[400] 70.08139 64.83928 69.07389 65.47385 65.08676 63.20714 65.14474
[407] 67.20394 67.58383 69.93506 69.22523 72.83923 64.96710 62.18814
[414] 69.44667 71.04231 65.58597 65.33885 69.13455 66.70100 68.19908
[421] 66.06285 61.32081 69.90240 67.30720 66.57178 61.04264 61.80940
[428] 63.75914 67.28085 61.75570 71.01266 66.52825 61.85719 68.28962
[435] 65.59296 64.33732 65.63342 65.00077 65.75227 62.78627 69.37201
[442] 68.91997 62.03314 62.76255 61.16370 71.63283 66.37910 64.68682
[449] 68.32264 67.58297 69.41408 62.11203 58.25285 68.11900 65.58488
[456] 69.08872 63.81261 65.39539 67.66209 62.81596 64.40207 70.72860
[463] 67.13486 69.24323 74.35509 66.78653 68.96963 67.31075 66.66942
[470] 60.94447 66.18825 69.74146 62.40750 64.03192 66.55163 68.97775
[477] 71.23874 65.88018 62.67145 68.72180 60.88645 61.47399 69.40619
[484] 68.03467 73.97748 64.52975 61.25330 66.11839 65.52646 64.45452
[491] 65.22356 71.24555 66.39973 66.24171 65.59767 64.05062 61.89873
[498] 62.59738 66.06177 66.56304
Plot Regression
ggplot(data = people, aes(x = height)) +
geom_point(aes(y = armspan)) +
geom_line(aes(y = rline, color = 'regression line'))
What is Our Regression Formula/Line?
It describes the relationship between the response variable (armspan) and the dependent variable (height). In other words, it describes how armspan is expected to change when height changes.
How It Was Created
We did not explicitly program the relationship with rules, we used an alogorithm that could infer the relationship based on example data.
Does The Relationship Hold?
Let's assume that was real data from American adults (18-50) measured in the last 10 years…
- Is the relationship valid for adults (18-50) in France?
- Is the relationship valid for children?
- Is the relationship valid for senior citizens?
- Is the relationship valid for ancient Neanderthal people?
This Is Machine Learning
The “rules” of our model weren't program in if-else like statements by a programmer, they were patterns recognized by an algorithm from data.
The model to describe the relationship can be used to to predict the response variable, based on the input variable(s). It's not a perfect predictor, but can be a useful prediction. Among other considerations, it's best that you're trying to predict on a subject that is of the same type as the data used to train the model.
As an example, our model for American adults should not be applied to children, as they were not represented in the set and have meaningful physiological differences.
So Let's Make a Prediction
We meet someone new with a height of 70 inches. What does our model predict their armspan is?
new_person = list(height = 70)
predict(reg_form, newdata = new_person)
1
69.90918
A Word on Data Sets
When Supervised Learning…
Train
Data used to train and tune the algorithm
Validation
Data used to validate the model being built
Test
Data used with final model to estimate model fit
Caret Package
- Name is short for Classification And REgression Training
Contains tools for:
- data splitting
- pre-processing
- feature selection
- model tuning using resampling
- variable importance estimation
Regression: Predicting HP
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Select Continuous Columns
x <- mtcars[, c('hp', 'mpg', 'disp', 'wt', 'qsec')]
head(x)
hp mpg disp wt qsec
Mazda RX4 110 21.0 160 2.620 16.46
Mazda RX4 Wag 110 21.0 160 2.875 17.02
Datsun 710 93 22.8 108 2.320 18.61
Hornet 4 Drive 110 21.4 258 3.215 19.44
Hornet Sportabout 175 18.7 360 3.440 17.02
Valiant 105 18.1 225 3.460 20.22
Let's Look At Correlation
pairs(x)
Split Our Data
library(caret)
training_index <- createDataPartition(x$hp, p = 0.7, list = FALSE)
train_data <- x[training_index, ]
test_data <- x[-training_index, ] # note the '-' in front of the index
dim(train_data)
[1] 24 5
Create Our Model
model <- train(hp ~ .,
data = train_data,
method = "lm")
summary(model)
Call:
lm(formula = .outcome ~ ., data = dat)
Residuals:
Min 1Q Median 3Q Max
-34.64 -15.73 -4.13 15.43 43.34
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 349.6838 77.1400 4.533 0.000227 ***
mpg -1.7725 2.1070 -0.841 0.410671
disp 0.2113 0.1204 1.756 0.095270 .
wt 5.6126 16.5219 0.340 0.737806
qsec -13.3088 3.9790 -3.345 0.003404 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 24.76 on 19 degrees of freedom
Multiple R-squared: 0.8545, Adjusted R-squared: 0.8239
F-statistic: 27.9 on 4 and 19 DF, p-value: 1.015e-07
Predict On Test Data
test_data$predicted <- predict(model, test_data)
test_data$miss <- test_data$predicted - test_data$hp
test_data[, c('hp', 'predicted', 'miss')]
hp predicted miss
Mazda RX4 Wag 110 135.89127 25.891270
Duster 360 245 209.63361 -35.366395
Merc 450SE 180 170.16383 -9.836174
Fiat 128 66 62.11056 -3.889439
Honda Civic 52 74.38162 22.381618
Pontiac Firebird 175 194.84054 19.840542
Maserati Bora 335 212.42861 -122.571387
Volvo 142E 109 105.38018 -3.619821
Measuring Error
library(ModelMetrics)
mse(test_data$hp, test_data$predicted)
[1] 2245.556
mae(test_data$hp, test_data$predicted)
[1] 30.42458
Housing Prices
Let's look at a larger size data set:
houses_raw <- read.csv("data/housing/train.csv")
houses <- houses_raw[, c('LotArea', 'Neighborhood',
'YearBuilt', 'TotRmsAbvGrd',
'FullBath', 'HalfBath',
'GrLivArea', 'TotalBsmtSF',
'SalePrice')]
dim(houses)
[1] 1460 9
Categorical Features
class(houses$Neighborhood)
[1] "factor"
summary(houses$Neighborhood)
Blmngtn Blueste BrDale BrkSide ClearCr CollgCr Crawfor Edwards Gilbert
17 2 16 58 28 150 51 100 79
IDOTRR MeadowV Mitchel NAmes NoRidge NPkVill NridgHt NWAmes OldTown
37 17 49 225 41 9 77 73 113
Sawyer SawyerW Somerst StoneBr SWISU Timber Veenker
74 59 86 25 25 38 11
Cross Validation
[creative commons - wikimedia]
Cross Validation
folds <- 10
trctl <- trainControl(method = "cv", number = folds, savePredictions = TRUE)
model <- train(SalePrice ~ .,
data = houses,
method = "lasso", # another regression model
trControl = trctl,
preProcess = c('center', 'scale'))
Examine Housing Results
mae(houses$SalePrice, model$pred$pred)
[1] 71168.93
A Word On Centering
center <- function(data) {
return(data - mean(data))
}
center(0:10)
[1] -5 -4 -3 -2 -1 0 1 2 3 4 5
A Word On Scaling
scale <- function(data) {
data <- data - min(data)
return(data / max(data))
}
scale(0:10)
[1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Combined
data <- runif(10, min = 1e4, max = 5e6)
data
[1] 2929670.0 2316389.7 2166179.7 227675.9 993271.3 4907249.0 2028033.9
[8] 4248921.2 297865.0 953574.8
center(scale(data))
[1] 0.17582522 0.04477046 0.01267137 -0.40157662 -0.23797294
[6] 0.59842338 -0.01684964 0.45774222 -0.38657759 -0.24645586
Preprocessing
Caret just handled that with:
preProcess = c('center', 'scale')
Missing Data
Caret can impute missing values:
preProcess = c('knnImpute')
Your mileage may vary with auto-imputing, you will often want to think through how your are imputing
Classification
actual <- factor(c('Hero', 'Hydra', 'Shield', 'Cannon Fodder', 'Hydra'))
predicted <- factor(c('Hero', 'Shield', 'Hydra', 'Cannon Fodder', 'Hydra'))
table(actual, predicted)
predicted
actual Cannon Fodder Hero Hydra Shield
Cannon Fodder 1 0 0 0
Hero 0 1 0 0
Hydra 0 0 1 1
Shield 0 0 1 0
Classification
iris data set
head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
SVM for Classification
Support Vector Machine
trctl <- trainControl(method="cv", repeats=3,
savePredictions = TRUE)
model <- train(Species ~ .,
data = iris,
method = "svmRadial",
trControl = trctl,
preprocess = c('scale'))
SVM Results
Confusion Matrix
#prefix with library, but ModelMetrics also has a confusion matrix function
caret::confusionMatrix(model$pred$obs, model$pred$pred)$table
Reference
Prediction setosa versicolor virginica
setosa 147 2 1
versicolor 0 144 6
virginica 0 9 141
What Feature mattered?
Caret will assess variable importance:
caret::varImp(model)
ROC curve variable importance
variables are sorted by maximum importance across the classes
setosa versicolor virginica
Petal.Length 100.00 100.00 100.0
Petal.Width 100.00 100.00 100.0
Sepal.Length 90.70 59.30 90.7
Sepal.Width 54.59 54.59 0.0
Unsupervised Learning
The algorithm looks for patterns without answers. This is useful for exploring your data.
Clustering with labelled classification data before doing supervised learning, can be a great way to decide if the categories you selected may have overlap, or other issues.
Projection
Unsupervised Learning
Let's load up Satellite image data that could be used in a classification problem
library(mlbench)
library(tsne)
data("Satellite")
dim(Satellite)
[1] 6435 37
Clustering Satellite Data
What fields to do we have?
names(Satellite)
[1] "x.1" "x.2" "x.3" "x.4" "x.5" "x.6" "x.7"
[8] "x.8" "x.9" "x.10" "x.11" "x.12" "x.13" "x.14"
[15] "x.15" "x.16" "x.17" "x.18" "x.19" "x.20" "x.21"
[22] "x.22" "x.23" "x.24" "x.25" "x.26" "x.27" "x.28"
[29] "x.29" "x.30" "x.31" "x.32" "x.33" "x.34" "x.35"
[36] "x.36" "classes"
Clustering Satellite Data
What does our input data look like?
class(Satellite$x.10)
[1] "numeric"
summary(Satellite$x.10)
Min. 1st Qu. Median Mean 3rd Qu. Max.
27.00 71.00 85.00 82.89 102.00 130.00
Clustering Satellite Data
What does our label data look like?
class(Satellite$classes)
[1] "factor"
summary(Satellite$classes)
red soil cotton crop grey soil
1533 703 1358
damp grey soil vegetation stubble very damp grey soil
626 707 1508
Split into X, y
In preparation for projecting…
We're going to use principle components and tsne to create a 2d projection of 36 dimensional data.
X <- Satellite[, -37] #drop last column
y <- Satellite$classes
Project Into Lower Dimensions
- project with principle components down to 4 dimensions
- project with that to 2 dimensions with tsne
pca <- princomp(X)
X_t <- predict(pca, X)[,1:2]
data <- data.frame(X_t,
y = as.factor(y))
data
Comp.1 Comp.2 y
1 -38.46692577 -33.334869060 grey soil
2 -33.87146415 -15.629939294 grey soil
3 -32.96125991 -13.178900238 grey soil
4 -29.89770016 -9.401104505 grey soil
5 -26.87398013 -4.257848873 grey soil
6 -29.91296837 -5.560905159 grey soil
7 -25.94906956 -18.510702578 grey soil
8 -26.33933578 -16.387504448 grey soil
9 -21.66933451 2.089131305 damp grey soil
10 -27.47464751 -0.127040499 damp grey soil
11 -28.16646534 3.540188506 damp grey soil
12 -21.42795544 10.670304062 damp grey soil
13 -16.78050479 6.422155296 damp grey soil
14 -20.22476818 9.170145465 damp grey soil
15 -17.60327895 23.267824052 damp grey soil
16 -14.71715811 17.806675939 damp grey soil
17 -14.51649975 9.756739425 damp grey soil
18 -19.15842181 -4.683341656 damp grey soil
19 -24.03295023 -18.301537536 grey soil
20 -36.80993948 -35.572194257 grey soil
21 -19.34197516 -22.667144066 grey soil
22 -21.93090613 -23.836821501 grey soil
23 -38.80687538 -32.120363891 grey soil
24 -41.21092282 -46.365027108 grey soil
25 -28.87649653 -27.474204589 grey soil
26 -25.85575869 -26.044166222 grey soil
27 -31.03541102 -28.450857694 grey soil
28 -35.53588963 -34.932481231 grey soil
29 -38.75720813 -40.917240623 grey soil
30 -39.78238354 -32.130934799 grey soil
31 -37.82189074 -26.805439512 grey soil
32 -29.19341326 -17.966565260 grey soil
33 -33.86007611 -18.102199966 grey soil
34 -32.94661532 -17.771234131 grey soil
35 -31.35586206 -18.944346674 grey soil
36 -36.67281292 -26.217090498 grey soil
37 -38.83849400 -29.666571625 grey soil
38 -32.33436101 -28.962709061 grey soil
39 -34.12741108 -31.897864492 grey soil
40 -36.14888771 -28.439200569 grey soil
41 -35.30552101 -25.515811252 grey soil
42 -30.37619149 -16.930923869 grey soil
43 -27.68314462 -11.210442987 grey soil
44 -19.42986443 -4.929506852 vegetation stubble
45 -8.40202323 6.635816834 vegetation stubble
46 10.76996545 31.095866276 vegetation stubble
47 -8.21367090 4.113513299 very damp grey soil
48 -26.93114763 2.035183974 very damp grey soil
49 -11.30814923 36.151095404 very damp grey soil
50 -3.57631926 43.567339139 very damp grey soil
51 -1.80925231 34.674063241 vegetation stubble
52 15.09160862 42.298851880 vegetation stubble
53 25.51211756 56.403784304 vegetation stubble
54 27.43655336 60.164455173 vegetation stubble
55 -48.30254036 -59.426164510 grey soil
56 -39.92571978 -27.817634065 grey soil
57 -33.75209393 -13.794301098 grey soil
58 -31.57425130 -12.802597860 grey soil
59 -28.11266163 -12.878587288 grey soil
60 -22.71288976 -25.769599525 grey soil
61 -26.52308334 -34.295986836 grey soil
62 -32.65187923 -37.139823474 grey soil
63 -34.04763424 -33.503986617 grey soil
64 -23.31701550 -0.787807291 damp grey soil
65 -24.81535226 -2.938653082 damp grey soil
66 -22.61668087 -2.280071388 damp grey soil
67 -22.96662888 -0.477088754 damp grey soil
68 -19.90266866 3.306063252 damp grey soil
69 -15.02421257 27.030988551 damp grey soil
70 -14.14204815 29.818172814 damp grey soil
71 -15.96902640 26.548963440 damp grey soil
72 -15.02421257 27.030988551 damp grey soil
73 -22.38602178 0.457519522 damp grey soil
74 -26.82257530 -14.695317756 grey soil
75 -32.06467430 -21.589154453 grey soil
76 -32.43680161 -42.132716946 grey soil
77 -36.37096286 -34.186052655 grey soil
78 -29.00715841 -20.276404307 grey soil
79 -31.02372379 -25.537120235 grey soil
80 -34.65160245 -38.273111916 grey soil
81 -38.78272680 -44.593334103 grey soil
82 -39.17558244 -49.780607287 grey soil
83 -38.25692433 -52.230534386 grey soil
84 -40.81064777 -43.605072040 grey soil
85 -38.59092792 -33.471700666 grey soil
86 -37.43569382 -33.047029792 grey soil
87 -40.90864500 -41.345025084 grey soil
88 -40.36371122 -45.868209127 grey soil
89 -36.64670697 -39.336481152 grey soil
90 -36.24130945 -39.139553375 grey soil
91 -34.98155768 -38.496853227 grey soil
92 -34.64156985 -31.601629462 grey soil
93 -31.57882151 -30.146601771 grey soil
94 -34.55470142 -24.230542945 grey soil
95 -31.17897557 -18.800841881 grey soil
96 -32.43761321 -19.428853160 grey soil
97 -31.15844297 -21.252944879 grey soil
98 -30.39358429 -25.358536914 grey soil
99 -35.56851672 -30.234389403 grey soil
100 -36.00317487 -27.955322605 grey soil
101 -33.81931918 -26.980146784 grey soil
102 -32.01964204 -24.174475383 grey soil
103 -27.02499442 -4.743419301 grey soil
104 -11.10955710 13.324550601 grey soil
105 -11.67793767 -3.728487691 grey soil
106 -21.08837735 -3.967055792 very damp grey soil
107 -18.51152287 5.919034841 very damp grey soil
108 -12.32450234 30.298925012 very damp grey soil
109 -14.88374149 35.299342477 very damp grey soil
110 -12.09361754 38.600546959 vegetation stubble
111 -1.42082761 41.239994622 vegetation stubble
112 15.27211282 49.259950025 vegetation stubble
113 23.08997532 59.252793698 vegetation stubble
114 19.60408540 63.785073100 vegetation stubble
115 19.93620909 65.860540135 vegetation stubble
116 -54.62441647 -84.672958323 grey soil
117 -55.74660930 -88.224513922 grey soil
118 -46.16428786 -73.397477973 grey soil
119 -32.95744615 -22.062883007 grey soil
120 -31.89827557 -12.354511653 grey soil
121 -34.06072437 -13.332293067 grey soil
122 -33.82669123 -20.379324869 grey soil
123 -31.47578999 -26.484242351 grey soil
124 -31.81677363 -14.525628334 grey soil
125 -29.10124679 -8.919448551 damp grey soil
126 -26.72506504 -1.506901207 damp grey soil
127 -25.10773532 -2.667071160 damp grey soil
128 -22.04624651 0.760508467 damp grey soil
129 -23.99493942 14.233371255 damp grey soil
130 -21.85410905 10.001821465 damp grey soil
131 -21.83132250 19.820544583 damp grey soil
132 -18.86742776 30.150042255 damp grey soil
133 -10.86823164 37.706890765 cotton crop
134 -7.96666436 34.741582468 cotton crop
135 -0.02220907 20.818341761 cotton crop
136 -25.72252248 -21.100754229 cotton crop
137 -25.63378732 -26.004064366 grey soil
138 -29.40712151 -23.351273967 grey soil
139 -34.63145960 -27.761991281 grey soil
140 -37.23814472 -28.926879793 grey soil
141 -34.10086239 -23.147551278 grey soil
142 -30.64792677 -23.981144059 grey soil
143 -32.80918997 -27.429214799 grey soil
144 -31.20767294 -28.606442773 grey soil
145 -33.59848024 -34.649156090 grey soil
146 -37.36283134 -37.539474411 grey soil
147 -38.91968868 -38.344708409 grey soil
148 -43.02700794 -44.647208970 grey soil
149 -41.04740213 -46.116420421 grey soil
150 -38.20503648 -39.378338140 grey soil
151 -39.60758395 -34.643266310 grey soil
152 -37.11195612 -32.451034682 grey soil
153 -35.55703227 -31.668982023 grey soil
154 -37.13762188 -29.385905936 grey soil
155 -37.13762188 -29.385905936 grey soil
156 -34.97517308 -28.408124522 grey soil
157 -36.54632849 -29.210371398 grey soil
158 -37.35481613 -34.449596997 grey soil
159 -37.04751530 -36.293216795 grey soil
160 -36.46212180 -33.031230985 grey soil
161 -38.08730525 -29.397672050 grey soil
162 -33.25170185 -22.676759020 grey soil
163 -28.14345883 -13.374617166 grey soil
164 -30.09325508 -7.574958117 grey soil
165 -29.48142908 -9.851526576 grey soil
166 -32.54185500 -13.633550273 grey soil
167 -34.19785092 -22.687396825 grey soil
168 -31.08837876 -19.249480202 grey soil
169 -31.10271705 -19.244617990 grey soil
170 -33.48862835 -11.176042837 grey soil
171 -25.98204600 -7.927644405 grey soil
172 -24.72110862 -9.755233583 grey soil
173 -21.32806202 -7.813295683 grey soil
174 -8.10773309 6.643476301 grey soil
175 -29.35351950 -15.990375372 grey soil
176 -28.52576610 -8.760972034 grey soil
177 -23.94697986 6.099570516 grey soil
178 -18.72563466 18.339690251 very damp grey soil
179 -12.91962239 27.372831691 very damp grey soil
180 -6.38942161 36.825401376 very damp grey soil
181 0.81971510 40.157846644 vegetation stubble
182 10.22213912 44.157611610 vegetation stubble
183 19.75041781 46.691038557 vegetation stubble
184 24.87073450 52.980718957 vegetation stubble
185 25.26265111 63.542361608 vegetation stubble
186 27.83292126 66.765963501 vegetation stubble
187 -38.78101960 -35.560991281 grey soil
188 -38.64510113 -48.890834475 grey soil
189 -43.97571212 -62.915747084 grey soil
190 -37.46603704 -44.515278162 grey soil
191 -28.50984888 -19.488588086 grey soil
192 -31.07943293 -14.480918993 grey soil
193 -30.14155646 -12.000028402 grey soil
194 -27.06699306 5.912253670 damp grey soil
195 -25.96244060 13.256909639 damp grey soil
196 -24.33412332 9.619122740 damp grey soil
197 -22.20491911 10.587061219 damp grey soil
198 -20.71915599 17.680457009 damp grey soil
199 -23.92039416 22.489497638 damp grey soil
200 -23.25605832 18.842931746 damp grey soil
201 -25.76598372 20.023705346 damp grey soil
202 -22.01631596 19.827245908 damp grey soil
203 -15.13882604 22.753237275 damp grey soil
204 -14.96635804 33.010895801 cotton crop
205 -5.55947958 29.133588315 cotton crop
206 13.65310592 25.104500116 cotton crop
207 39.61297086 11.187241061 cotton crop
208 86.15111239 -18.897907596 cotton crop
209 86.51065797 -21.219022964 cotton crop
210 -4.12993186 -2.379598568 cotton crop
211 -32.82010190 -26.036484806 grey soil
212 -35.00351691 -27.008179443 grey soil
213 -34.07436614 -24.544310332 grey soil
214 -34.07436614 -24.544310332 grey soil
215 -39.61436744 -36.014725518 grey soil
216 -38.78101960 -35.560991281 grey soil
217 -34.68662147 -37.976011758 grey soil
218 -34.71193569 -37.983660984 grey soil
219 -38.78101960 -35.560991281 grey soil
220 -37.35824298 -32.529361664 grey soil
221 -32.45876084 -30.644043162 grey soil
222 -31.82601473 -30.184154299 grey soil
223 -32.96972468 -15.307558736 grey soil
224 -31.33747261 -18.938861052 grey soil
225 -35.44864835 -31.404076283 grey soil
226 -36.56105454 -36.275343270 grey soil
227 -34.21788596 -33.237525968 grey soil
228 -31.67569854 -17.450119671 grey soil
229 -32.37077444 -11.017164387 grey soil
230 -29.89202270 -11.857564815 grey soil
231 -28.26953947 -13.040879565 grey soil
232 -28.91698413 -13.503137580 grey soil
233 -27.70860769 -10.885870178 grey soil
234 -29.57465183 -9.859671343 grey soil
235 -29.23572561 -11.669175101 grey soil
236 -24.84110436 -13.869513879 grey soil
237 -29.07043414 -26.232944244 grey soil
238 -27.96634900 -17.326363613 grey soil
239 -23.41269062 7.682413905 very damp grey soil
240 -15.26706465 26.401751602 very damp grey soil
241 -11.51504818 28.676709800 very damp grey soil
242 -11.29352794 34.790749896 very damp grey soil
243 1.38095239 44.613879771 very damp grey soil
244 7.98434803 52.983497638 very damp grey soil
245 13.99843284 55.830739777 vegetation stubble
246 20.26284145 52.997860335 very damp grey soil
247 25.99803843 54.736676320 vegetation stubble
248 -37.75178040 -36.511636732 grey soil
249 -36.58026318 -30.958931105 grey soil
250 -33.39910595 -33.787538119 grey soil
251 -33.37748867 -58.014966674 grey soil
252 -43.16978955 -79.484546157 grey soil
253 -44.74744051 -56.065283680 grey soil
254 -27.79191417 -18.545042237 grey soil
255 -25.50191698 -10.678330960 grey soil
256 -28.15770705 -1.241004495 grey soil
257 -28.24776758 4.623358030 damp grey soil
258 -27.33604870 7.094442228 damp grey soil
259 -27.94585662 12.735831253 damp grey soil
260 -27.57776815 15.293387842 damp grey soil
261 -26.43056436 12.014642301 damp grey soil
262 -26.07006562 14.570211267 damp grey soil
263 -22.54493762 17.654520001 damp grey soil
264 -17.82819480 17.464486681 damp grey soil
265 -20.01717985 16.471887589 damp grey soil
266 -22.47335095 11.593251262 damp grey soil
267 -20.98973683 8.390261044 cotton crop
268 -17.51082511 22.131510499 cotton crop
269 4.25767083 18.930056538 cotton crop
270 56.92011832 -14.917600117 cotton crop
271 83.41405239 -24.491741292 cotton crop
272 96.15147495 -27.661840209 cotton crop
273 99.04220112 -28.753554632 cotton crop
274 28.60460973 3.146429539 cotton crop
275 -16.05353501 -1.971935982 grey soil
276 -30.12710232 -19.239901790 grey soil
277 -27.21819687 -18.523285101 grey soil
278 -28.43870546 -20.859741023 grey soil
279 -31.32580353 -21.573607032 grey soil
280 -35.41882472 -27.879094715 grey soil
281 -37.67293940 -35.818425027 grey soil
282 -39.23977538 -39.095189193 grey soil
283 -37.08788507 -33.994275891 grey soil
284 -38.36425217 -32.190157997 grey soil
285 -31.03804077 -30.759790022 grey soil
286 -36.24574489 -32.785385024 grey soil
287 -41.20341358 -31.167072313 grey soil
288 -36.02668964 -21.998839803 grey soil
289 -36.74122745 -27.351362740 grey soil
290 -33.45098958 -23.297519594 grey soil
291 -32.27415729 -20.203761380 grey soil
292 -38.65865751 -34.802936032 grey soil
293 -38.36816945 -32.184873042 grey soil
294 -36.00164412 -35.468791030 grey soil
295 -31.82191743 -24.182049848 grey soil
296 -31.93346736 -16.201111562 grey soil
297 -28.35674144 -7.673401275 grey soil
298 -24.58610748 -7.875947490 grey soil
299 -26.39428885 -13.131228829 grey soil
300 -29.81217000 -17.226227681 grey soil
301 -29.93570619 -8.967193131 grey soil
302 -28.69810621 -5.848116880 grey soil
303 -27.96485602 -9.820872512 grey soil
304 -25.85230359 -10.989049498 grey soil
305 -29.81217000 -17.226227681 grey soil
306 -31.01753934 -19.556833268 grey soil
307 -33.21591458 -18.096433509 grey soil
308 -34.14845565 -16.709529836 grey soil
309 -34.14060191 -19.182918818 grey soil
310 -14.05394857 33.495173899 very damp grey soil
311 -10.04150247 39.261113853 very damp grey soil
312 -0.12648138 60.916042327 very damp grey soil
313 2.43540850 55.916471094 very damp grey soil
314 8.45619492 54.834647160 very damp grey soil
315 -44.84868441 -57.454021738 grey soil
316 -43.16437847 -56.612489365 grey soil
317 -40.99761257 -70.124750393 grey soil
318 -48.73568672 -76.079025505 grey soil
319 -47.50496530 -68.300279065 grey soil
320 -27.81203872 -20.991438909 grey soil
321 -29.49978052 -19.358171913 grey soil
322 -32.47789591 -14.543202634 grey soil
323 -32.15533366 -12.529415991 grey soil
324 -28.20681895 -10.661501955 damp grey soil
325 -26.35358055 -1.583682161 damp grey soil
326 -26.24739089 10.938910652 damp grey soil
327 -24.64199304 5.173464216 damp grey soil
328 -25.31435930 8.814561302 damp grey soil
329 -25.32102743 8.817660957 damp grey soil
330 -17.84779912 14.998386507 cotton crop
331 -6.96303137 11.502512240 cotton crop
332 73.17917530 -16.996113782 cotton crop
333 90.54487483 -25.944218682 cotton crop
334 99.62202647 -32.639745815 cotton crop
335 104.53264691 -35.285829070 cotton crop
336 99.82814915 -32.506515420 cotton crop
337 101.20497603 -36.157402411 cotton crop
338 101.85137749 -34.062434080 cotton crop
339 12.62883464 0.528934817 cotton crop
340 -25.35062484 -11.254455875 grey soil
341 -31.27087191 -22.766263253 grey soil
342 -29.94374858 -19.745866489 grey soil
343 -26.70705749 -15.760572047 grey soil
344 -41.31354371 -39.992330759 grey soil
345 -46.18700571 -42.194877635 grey soil
346 -43.59343306 -43.497291329 grey soil
347 -41.14891787 -40.510667455 grey soil
348 -38.54771012 -34.999433682 grey soil
349 -36.18513031 -31.951640443 grey soil
350 -34.30125379 -32.979698091 grey soil
351 -38.21570960 -38.999796109 grey soil
352 -39.18185540 -37.664199500 grey soil
353 -37.96659225 -30.707180521 grey soil
354 -33.95086717 -32.668979554 grey soil
355 -33.93166418 -36.502694318 grey soil
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1676 0.38443491 27.106391878 vegetation stubble
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1851 6.82674213 -3.763506346 cotton crop
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1859 33.70311950 17.253966765 damp grey soil
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1870 -35.21813010 -29.923670866 damp grey soil
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1893 -12.34761809 34.938426009 very damp grey soil
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1897 -17.56064734 28.357332174 very damp grey soil
1898 -19.64653332 20.460955582 very damp grey soil
1899 -22.24763747 8.586277668 very damp grey soil
1900 -22.74061754 5.973606888 very damp grey soil
1901 -21.93530953 6.763486333 very damp grey soil
1902 -19.21941508 12.003669373 damp grey soil
1903 -18.23351427 29.393900426 damp grey soil
1904 60.82377226 -2.145540659 cotton crop
1905 80.76100255 6.696452189 cotton crop
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1908 105.48695856 -18.494278688 cotton crop
1909 99.52717750 -12.195254516 cotton crop
1910 77.37428093 1.351701936 cotton crop
1911 35.64321879 16.205889988 cotton crop
1912 -47.56950629 -54.106060164 grey soil
1913 -43.18400244 -59.455916489 grey soil
1914 -42.58537034 -51.849162154 grey soil
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1916 -35.38114677 -21.338148428 damp grey soil
1917 -28.09670215 -11.020375218 damp grey soil
1918 -30.21952870 -12.009034302 damp grey soil
1919 -38.40209356 -27.559445066 damp grey soil
1920 -35.20550385 -32.384302873 damp grey soil
1921 -35.54726910 -30.564374752 damp grey soil
1922 -34.89269801 -19.060258105 very damp grey soil
1923 -21.61294265 22.413946317 very damp grey soil
1924 -16.19507440 49.014790121 very damp grey soil
1925 -9.86263099 59.840938574 very damp grey soil
1926 -12.69909056 55.030042675 very damp grey soil
1927 -10.26846857 49.937666321 very damp grey soil
1928 -13.15270767 48.560783881 very damp grey soil
1929 -11.53087107 49.294119941 very damp grey soil
1930 -11.78689465 50.663083237 very damp grey soil
1931 -17.72555603 47.971097776 very damp grey soil
1932 -18.74129184 45.956036323 very damp grey soil
1933 -17.48662712 44.469218121 very damp grey soil
1934 -11.45894437 48.841068041 very damp grey soil
1935 -10.77509363 52.671660108 very damp grey soil
1936 -15.93756076 51.089135341 very damp grey soil
1937 -14.92450055 57.488979764 very damp grey soil
1938 -15.25440543 54.942407501 very damp grey soil
1939 -10.78087818 52.675041840 very damp grey soil
1940 -10.41300880 52.762296020 very damp grey soil
1941 -9.44086991 45.289535823 very damp grey soil
1942 -11.05582624 35.663445205 very damp grey soil
1943 -14.85176448 33.926043543 very damp grey soil
1944 -16.10175308 34.387950666 very damp grey soil
1945 -16.10175308 34.387950666 very damp grey soil
1946 -15.08325680 28.865339604 very damp grey soil
1947 -18.16095878 25.415345683 very damp grey soil
1948 -15.32238060 19.947341757 damp grey soil
1949 -8.59002237 33.938914741 damp grey soil
1950 -7.30930439 34.575528112 damp grey soil
1951 -14.03447130 18.470366490 damp grey soil
1952 -9.26297807 8.027587519 damp grey soil
1953 28.66371629 -1.980408431 cotton crop
1954 86.11079321 0.551385992 cotton crop
1955 95.14281697 -8.467794407 cotton crop
1956 103.28712000 -18.384386897 cotton crop
1957 90.97468291 -11.954784753 cotton crop
1958 47.14728569 10.035919048 cotton crop
1959 -1.25117120 26.526391694 damp grey soil
1960 -13.83017315 29.511139407 damp grey soil
1961 -47.19324945 -55.422122702 grey soil
1962 -46.78313201 -57.694355943 grey soil
1963 -46.79315087 -60.164011023 grey soil
1964 -46.63585434 -47.825874908 grey soil
1965 -37.70314206 -30.705205579 damp grey soil
1966 -39.66236237 -22.775678312 damp grey soil
1967 -38.03201864 -23.944473737 damp grey soil
1968 -34.08866421 -24.541323210 damp grey soil
1969 -36.03015060 -19.089582433 damp grey soil
1970 -30.62809865 -25.374803458 damp grey soil
1971 -33.88777200 -31.226556903 damp grey soil
1972 -37.86125565 -41.813514898 damp grey soil
1973 -39.17214643 -42.462669066 damp grey soil
1974 -34.99753989 -22.975852920 very damp grey soil
1975 -28.86887410 -0.724526903 very damp grey soil
1976 -22.93212545 17.024862437 very damp grey soil
1977 -19.26608115 25.094180828 very damp grey soil
1978 -16.98342693 38.791925972 very damp grey soil
1979 -12.09971843 57.176670645 very damp grey soil
1980 -12.97587385 48.887861062 very damp grey soil
1981 -17.27591942 42.005677947 very damp grey soil
1982 -14.79868231 41.145232376 very damp grey soil
1983 -12.98545201 46.421687166 very damp grey soil
1984 -10.08621871 43.427841258 very damp grey soil
1985 -11.94817727 40.614819981 very damp grey soil
1986 -13.54678427 44.261321506 very damp grey soil
1987 -11.72570024 47.064387314 very damp grey soil
1988 -8.95138898 50.348533776 very damp grey soil
1989 -10.43598733 45.722256340 very damp grey soil
1990 -13.88814904 46.086605901 very damp grey soil
1991 -14.75954970 43.295306207 very damp grey soil
1992 -12.62721166 44.259016722 very damp grey soil
1993 -9.23372972 54.172981802 very damp grey soil
1994 -8.50966590 53.565680983 very damp grey soil
1995 -12.62390985 52.559918982 very damp grey soil
1996 -10.43111779 45.251997343 very damp grey soil
1997 -10.99849390 40.626586096 very damp grey soil
1998 -13.50937608 41.471832459 very damp grey soil
1999 -14.75169596 40.821917225 very damp grey soil
2000 -15.68084673 38.358048113 very damp grey soil
Plot
Plot the components on graph and color by label
qplot(Comp.1, Comp.2, color = y, data = data, size = I(.5))
Pipes
# Normal Syntax
a <- 1:5
sample(a, size = 2)
# Pipe Sytax
a %>% sample(size = 2)
What's the Benefit?
flights %>%
group_by(year, month, day) %>%
select(arr_delay, dep_delay) %>%
summarise(
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE)
) %>%
filter(arr > 30 | dep > 30)
Deep Learning
Demo (nn.R)
Building Great Models
- Feature Selection
- Imputing (deal with missing data)
- Model Tuning
- Algorithm Selection
- Statistical Learning
Suggested Resources
- Coursera Machine Learning Course by Andrew Ng
- Kaggle.com forums and tutorials
- My Machine Learning in R Videos on PacktPub.com