What is the Random Forest Algorithm?

• Random Forest is an ensemble machine-learning algorithm that follows the bagging technique.

• The base estimators in the random forest are decision trees. Random forest randomly selects a set of Features to decide the best split at each node of the decision tree.

Looking at it step-by-step, this is what a random forest model does:

1. Random subsets are created from the original dataset (bootstrapping).

2. At each node in the decision tree, only a random set of features is considered to decide the best split.

3. A decision tree model is fitted on each of the subsets.

4. The final prediction is calculated by averaging the predictions from all decision trees.

• To summarize, the Random forest randomly selects data points and features and builds multiple trees (Forest).

• Random Forest is used for feature importance selection.

• The attribute (.feature_importances_) is used to find feature importance.

Some Important Parameters:-

1. n_estimators:

   • It defines the number of decision trees created in a random forest.

2. criterion:

   • "Gini" or "Entropy."

3. min_samples_split:

   • It is used to define the minimum number of samples required in a leaf node before a split is attempted.

4. max_features:

   • It defines the maximum number of features allowed for the split in each decision tree.

5. n_jobs:

   • The number of jobs to run in parallel for both fits and predict. Always keep (-1) to use all the cores for parallel processing.