Ensemble learning is a machine learning technique that combines the predictions

Ensemble learning is a machine learning technique that combines the predictions from multiple models to create a more accurate and stable prediction.
It is an approach that leverages the collective intelligence of multiple models to improve the overall performance of the learning syste

1. R19AD251 - Data Science Ensemble Learning

2. Overview Ensemble learning is a machine learning technique that combines the predictions from multiple models to create a more accurate and stable prediction. It is an approach that leverages the collective intelligence of multiple models to improve the overall performance of the learning system.

3. Types of Ensemble Learning Bagging (Bootstrap Aggregating) Boosting

4. Bagging : (Bootstrap Aggregating) This technique involves training multiple models on different subsets of the data, often using random sampling with replacement. The final prediction is typically made by averaging (for regression) or voting (for classification) the predictions of all models. An example of bagging is the Random Forest algorithm.

5. Boosting In boosting, models are trained sequentially, with each model trying to correct the errors of the previous one.   The predictions of all models are combined, often through weighted voting, where more accurate models have a higher influence. Examples include AdaBoost and Gradient Boosting.

6. Bagging and boosting

7. Random Forest algorithm Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model. Random forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. Instead of relying on one decision tree, the random forest takes the prediction from each tree and based on the majority votes of predictions, and it predicts the final output.

8. Random Forest algorithm

9. Advantage of Random Forest algorithm High Accuracy Robustness to Noise Estimating Feature Importance: Handles Missing Data and Outliers Handles Both Numerical and Categorical Data

10. Disadvantage of Random Forest algorithm Computational Complexity Memory Usage Prediction Time Lack of Interpretability Overfitting

11. Reference Link https://www.kaggle.com/code/prashant111/random-forest-classifier-feature-importance https://www.kaggle.com/code/odaymourad/random-forest-model-clearly-explained https://www.kaggle.com/code/faressayah/decision-trees-random-forest-for-beginners https://www.kaggle.com/code/hashemialii/random-forest-classification-algorithm https://www.kaggle.com/code/dansbecker/random-forests