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You're reading from Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

Product type Paperback

Published in May 2019

Publisher

ISBN-13 9781838556334

Length 502 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (2):

Jojo Moolayil

Karthik Ramasubramanian

View More author details

Table of Contents (12) Chapters

Applied Supervised Learning with R

Preface

1. R for Advanced Analytics FREE CHAPTER

2. Exploratory Analysis of Data

3. Introduction to Supervised Learning

4. Regression

5. Classification

6. Feature Selection and Dimensionality Reduction

7. Model Improvements

8. Model Deployment

9. Capstone Project - Based on Research Papers

Appendix

Chapter 4: Regression

Activity 7: Printing Various Attributes Using Model Object Without Using the summary Function

First, print the coefficient values using the following command. Make sure the output is like the output of the summary function using the coefficients option. The coefficients are the fitted values from the model that uses the OLS algorithm:
```
multiple_PM25_linear_model$coefficients
```
The output is as follows:
```
(Intercept)        DEWP        TEMP         Iws 
161.1512066   4.3841960  -5.1335111  -0.2743375
```
Find the residual value (difference) of the predicted and actual values of PM2.5, which should be as small as possible. Residual reflects how far the fitted values using the coefficients are from the actual value.
```
multiple_PM25_linear_model$residuals
```
The output is as follows:
```
25            26            27            28 
  17.95294914   32.81291348   21.38677872   26.34105878 
           29            30            31            32 
```

Next, find the fitted values that should be closer to the actual PM2.5 for the best model. Using the coefficients, we can compute the fitted values:

multiple_PM25_linear_model$fitted.values

The output is as follows:

25         26         27         28         29 
111.047051 115.187087 137.613221 154.658941 154.414781 
        30         31         32         33         34

Find the R-Squared values. They should look the same as the one you obtained in the output of the summary function next to the text Multiple R-squared. R-Square helps in evaluating the model performance. If the value is closer to 1, the better the model is:
```
summary(multiple_PM25_linear_model)$r.squared
```
The output is as follows:
```
[1] 0.2159579
```
Find the F-Statistic values. Make sure the output should look same as the one you obtained in the output of the summary function next to the text F-Statistics. This will tell you if your model fits better than just using the mean of the target variable. In many practical applications, F-Statistic is used along with p-values:
```
summary(multiple_PM25_linear_model)$fstatistic
```
The output is as follows:
```
    value     numdf     dendf 
 3833.506     3.000 41753.000 
```
Finally, find the coefficient p-values and make sure the values should look the same as the one you obtained in the output of the summary function under Coefficients for each variable. It will be present under the column titled Pr(>|t|):. If the value is less than 0.05, the variable is statistically significant in predicting the target variable:
```
summary(multiple_PM25_linear_model)$coefficients[,4]
```
The output is as follows:
```
  (Intercept)          DEWP          TEMP           Iws 
 0.000000e+00  0.000000e+00  0.000000e+00 4.279601e-224
```

The attributes of a model are equally essential to understand, especially in linear regression than to obtain the prediction. They help in interpreting the model well and connect the problem to its real use case.