Random Ring

Price: ₹1500.00 ₹1200.0000 (-20.00%)
Availability: In Stock

Description:
Random Forest is an ensemble machine learning model that combines multiple decision trees to make predictions. Each tree is trained on a random subset of the training data (called a bootstrap sample) and a random subset of features. For classification, the final prediction is based on the majority vote of all trees, and for regression, it’s based on the average of all trees’ outputs. This reduces overfitting and increases accuracy.

When to Use:

• When data has non-linear relationships.

• When you want good performance without much parameter tuning.

• When you want to know which features are most important.

Advantages:

• Works well with both numerical and categorical data.

• Resistant to overfitting compared to single decision trees.

• Provides feature importance scores.

• Robust to noise and missing data.

Disadvantages:

• Can be slower to predict when there are many trees.

• Harder to interpret compared to simple models.

• May not perform well on very high-dimensional sparse data (like text).