> For the complete documentation index, see [llms.txt](https://docs.exploratory.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.exploratory.io/machine-learning/random-forest.md). # 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. ![](/files/-M4oNEYPJEZvgANFY8n-) Another way is to access from a column header menu. ![](/files/-M4oNEYSq0jU1uesr-7z) ## How to Use? ### Column Selection ![](/files/-M4oNEYW5uXoYt6UVrvF) There are two ways to set what you want to predict by what variables. ![](/files/-M4oNDzH4u0X4wvDSMOT) If you are on "Select Columns" tab, you can set them by column selector. ![](/files/-M4oNDzJrXqnrD85G44U) If you are on "Custom" tab, you can type a formula directly. ### Train Test Split ![](/files/-M4oNDzLGK7ZU1RCNZk_) 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 ![](/files/-M4oNEYdZVmnhweZBNhF) **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 ![](/files/-M4oNEYhmhaIuy2xAnxB) **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 ![](/files/-M4oNEYkJ2_zx6EgXQLb) **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.