Here’s a step-by-step explanation of how Random Forest works:​

Step 1: Understand the Data

You need a dataset with:

• Features (X): The input variables or predictors.
• Target (Y): The output variable or label you want to predict.

Step 2: Create Bootstrap Samples

Random Forest builds multiple decision trees using different subsets of the training data. These subsets are created by bootstrapping:

• Bootstrap Sampling: Randomly sample from the dataset with replacement to create multiple training subsets. Each subset is the same size as the original dataset but may contain duplicate records.

Step 3: Build Decision Trees

For each bootstrap sample:

1. Train a Decision Tree: Construct a decision tree using the bootstrap sample.
2. Feature Randomness: When splitting nodes in each decision tree, randomly select a subset of features rather than considering all features. This helps to ensure that the trees are diverse and reduces correlation between them.

Step 4: Aggregate the Trees

Once all the trees are built:

• For Classification: Each tree in the forest votes for a class label. The class with the majority vote across all trees is the final prediction.

  Example:

  • Tree 1: Class A
  • Tree 2: Class B
  • Tree 3: Class A
  • Majority vote: Class A

• For Regression: The prediction is the average of all the trees' predictions.

  Example:

  • Tree 1 predicts 3.0
  • Tree 2 predicts 3.5
  • Tree 3 predicts 2.8
  • Average prediction: (3.0 + 3.5 + 2.8) / 3 = 3.1

Step 5: Evaluate the Model

Evaluate the Random Forest model using metrics such as:

• Accuracy: For classification, the proportion of correctly classified samples.
• Confusion Matrix: For classification, details of true positives, true negatives, false positives, and false negatives.
• Mean Absolute Error (MAE): For regression, the average absolute error between predicted and actual values.
• R-squared (R²): For regression, the proportion of variance in the dependent variable that is predictable from the independent variables.

Step 6: Tune Hyperparameters (Optional)

Fine-tune the performance of the Random Forest by adjusting hyperparameters:

• Number of Trees (n_estimators): The number of decision trees in the forest. More trees usually improve performance but increase computation time.
• Maximum Depth (max_depth): The maximum depth of each tree. Limiting depth can prevent overfitting.
• Minimum Samples Split (min_samples_split): The minimum number of samples required to split an internal node.
• Minimum Samples Leaf (min_samples_leaf): The minimum number of samples required to be at a leaf node.
• Number of Features (max_features): The number of features to consider when looking for the best split.