# Random Forest

## Introduction

Create random forest model for regression, binary classification and multiclass classification.

## How to Access?

There are two ways to access. One is to access from 'Add' (Plus) button.

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNEYPJEZvgANFY8n-%2Frandom_forest_add.png?generation=1586795494752262\&alt=media)

Another way is to access from a column header menu.

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNEYSq0jU1uesr-7z%2Frandom_forest_cols.png?generation=1586795503078037\&alt=media)

## How to Use?

### Column Selection

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNEYW5uXoYt6UVrvF%2Frandom_forest_dialog.png?generation=1586795487845773\&alt=media)

There are two ways to set what you want to predict by what variables.

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNDzH4u0X4wvDSMOT%2Ffml_col_selection.png?generation=1586795485601070\&alt=media)

If you are on "Select Columns" tab, you can set them by column selector.

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNDzJrXqnrD85G44U%2Ffml_custom.png?generation=1586795485831641\&alt=media)

If you are on "Custom" tab, you can type a formula directly.

### Train Test Split

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNDzLGK7ZU1RCNZk_%2Ftrain_test_split.png?generation=1586795485645143\&alt=media)

You can split the data into training and test to evaluate the performance of the model. You can set

* Test Data Set Ratio - Ratio of test data in the whole data.
* Random Seed to Split Training/Test - You can change random seed to try other training and test data combination.

### Regression

#### Parameters

* A Vector to Subset Data (Optional) - Logical column to subset data.
* How to treat NA? (Optional) - The default is "Omit". This changes the behaviour of NA data. Can be one of the following.
  * Omit
  * Fail
  * Exclude
  * Pass
  * Null
* Number of Trees (Optional) - The default is 500. Number of trees to grow.
* Number of Variables to Sample (Optional) - The default is 1/3 of number of variables. Number of variables randomly sampled as candidates at each split.
* Sample with Replacement (Optional) - The default is TRUE. Whether sampling is done with replacement.
* Variable for Stratified Sampling (Optional) - A (factor) variable that is used for stratified sampling.
* Size of Sample to Draw (Optional) - Size of sample to draw.
* Minimum Size of Terminal Nodes (Optional) - The default is 5. Minimum size of terminal nodes.
* Assess Importance (Optional) - The default is FALSE. Whether importance of predictors is assessed.
* Compute Casewise Importance (Optional) - The default is FALSE. Whether casewise importance measure is computed.
* Measure Proximity (Optional) - The default is FALSE. Whether proximity measure among the rows is calculated.
* Proximity on Out-of-Bag Data (Optional) - The default is same as "Measure Proximity". Whether proximity is calculated only on "out-of-bag" data.
* Keep Forest (Optional) - The default is TRUE. Whether the forest is retained in the output object.
* Perform Bias Correction (Optional) - The default is FALSE. If bias correction for regression is performed.
* Keep In-Bag (Optional) - The default is FALSE. Whether an n by ntree matrix be returned that keeps track of which samples are “in-bag” in which trees.

#### How to Read Summary

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNEYdZVmnhweZBNhF%2Frf_regression_summary.png?generation=1586795494006050\&alt=media)

**Summary of Fit**

* Mean of Mean Square Error - Mean of mean square errors vector.
* Mean of R Square - Mean of pseudo R-squared.

**Feature Importance**

* Term - The term in the linear model being estimated and tested.
* Mean Decrease in Mean Square Error - Mean of square error decrease by the term.

### Binary Classification

#### Parameters

* A Vector to Subset Data (Optional) - Logical column to subset data.
* How to treat NA? (Optional) - The default is "Omit". This changes the behaviour of NA data. Can be one of the following.
  * Omit
  * Fail
  * Exclude
  * Pass
  * Null
* Number of Trees (Optional) - The default is 500. Number of trees to grow.
* Number of Variables to Sample (Optional) - The default is square root of number of variables. Number of variables randomly sampled as candidates at each split.
* Sample with Replacement (Optional) - The default is TRUE. Whether sampling is done with replacement.
* Priors of Classes (Optional) - Priors of the classes.
* A Vector for Cutoff (Optional) - A vector of length equal to number of classes. The ‘winning’ class for an observation is the one with the maximum ratio of proportion of votes to cutoff. Default is 1/k where k is the number of classes.
* Variable for Stratified Sampling (Optional) - A (factor) variable that is used for stratified sampling.
* Size of Sample to Draw (Optional) - Size of sample to draw.
* Minimum Size of Terminal Nodes (Optional) - The default is 1. Minimum size of terminal nodes.
* Assess Importance (Optional) - The default is FALSE. Whether importance of predictors is assessed.
* Compute Casewise Importance (Optional) - The default is FALSE. Whether casewise importance measure is computed.
* Number of Permutation (Optional) - The default is FALSE. Number of times the OOB data are permuted per tree for assessing variable importance.
* Measure Proximity (Optional) - The default is FALSE. Whether proximity measure among the rows is calculated.
* Proximity on Out-of-Bag Data (Optional) - The default is same as proximity. Whether proximity is calculated only on "out-of-bag" data.
* Normalize Votes (Optional) - The default is TRUE. Whether the final result of votes are expressed as fractions. If FALSE, raw vote counts are returned (useful for combining results from different runs).
* Keep Forest (Optional) - The default is TRUE. Whether the forest is retained in the output object.
* Perform Bias Correction (Optional) - The default is FALSE. If bias correction for regression is performed.
* Keep In-Bag (Optional) - The default is FALSE. Whether an n by ntree matrix be returned that keeps track of which samples are “in-bag” in which trees.

#### How to Read Summary

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNEYhmhaIuy2xAnxB%2Frf_binary_summary.png?generation=1586795488000388\&alt=media)

**Evaluation of Training Data**

* F Score - Harmonic mean of Precision and Recall. 2 \* Precision \* Recall / (Precision + Recall)
* Accuracy Rate - Ratio of correct classification in training data.
* Missclassification Rate - Ratio of incorrect classification in training data.
* Precision - How many relevant items are selected.
* Recall - How many selected items are relevant.

**Feature Importance**

* Term - The term in the linear model being estimated and tested.
* Mean Decrease Gini - Mean of Gini index decrease by the term.

### Multiclass Classification

#### Parameters

* A Vector to Subset Data (Optional) - Logical column to subset data.
* How to treat NA? (Optional) - The default is "Omit". This changes the behaviour of NA data. Can be one of the following.
  * Omit
  * Fail
  * Exclude
  * Pass
  * Null
* Number of Trees (Optional) - The default is 500. Number of trees to grow.
* Number of Variables to Sample (Optional) - The default is square root of number of variables. Number of variables randomly sampled as candidates at each split.
* Sample with Replacement (Optional) - The default is TRUE. Whether sampling is done with replacement.
* Priors of Classes (Optional) - Priors of the classes.
* A Vector for Cutoff (Optional) - A vector of length equal to number of classes. The ‘winning’ class for an observation is the one with the maximum ratio of proportion of votes to cutoff. Default is 1/k where k is the number of classes.
* Variable for Stratified Sampling (Optional) - A (factor) variable that is used for stratified sampling.
* Size of Sample to Draw (Optional) - Size of sample to draw.
* Minimum Size of Terminal Nodes (Optional) - The default is 1. Minimum size of terminal nodes.
* Assess Importance (Optional) - The default is FALSE. Whether importance of predictors is assessed.
* Compute Casewise Importance (Optional) - The default is FALSE. Whether casewise importance measure is computed.
* Number of Permutation (Optional) - The default is FALSE. Number of times the OOB data are permuted per tree for assessing variable importance.
* Measure Proximity (Optional) - The default is FALSE. Whether proximity measure among the rows is calculated.
* Proximity on Out-of-Bag Data (Optional) - The default is same as proximity. Whether proximity is calculated only on "out-of-bag" data.
* Normalize Votes (Optional) - The default is TRUE. Whether the final result of votes are expressed as fractions. If FALSE, raw vote counts are returned (useful for combining results from different runs).
* Keep Forest (Optional) - The default is TRUE. Whether the forest is retained in the output object.
* Perform Bias Correction (Optional) - The default is FALSE. If bias correction for regression is performed.
* Keep In-Bag (Optional) - The default is FALSE. Whether an n by ntree matrix be returned that keeps track of which samples are “in-bag” in which trees.

Take a look at the [reference document](http://ugrad.stat.ubc.ca/R/library/randomForest/html/randomForest.html) for the 'randomForest' function from randomForest package for more details on the parameters.

#### How to Read Summary

![](https://2850417076-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4HLCK3olgduYoe3RVS%2F-M4oMvCUDQwHTJ0eWi_f%2F-M4oNEYkJ2_zx6EgXQLb%2Frf_multi_summary.png?generation=1586795487768810\&alt=media)

**Evaluation of Training Data**

* Class - Label of the class.
* F Score - Harmonic mean of Precision and Recall. 2 \* Precision \* Recall / (Precision + Recall)
* Accuracy Rate - Ratio of correct classification in training data.
* Missclassification Rate - Ratio of incorrect classification in training data.
* Precision - How many relevant items are selected.
* Recall - How many selected items are relevant.
* Data Size - Number of Data

**Feature Importance**

* Term - The term in the linear model being estimated and tested.
* Mean Decrease Gini - Mean of Gini index decrease by the term.