In this video we will be discussing about the important interview questions on Random Forest algorithm.
Important Interview Questions:
- Decision Tree
- Entropy, Information Gain, Gini Impurity
- Decision Tree Working For Categorical and Numerical Features
- What are the scenarios where Decision Tree works well
- Decision Tree Low Bias And High Variance- Overfitting
- Hyperparameter Techniques
- Library used for constructing decision tree
- Impact of Outliers Of Decision Tree
- Impact of mising values on Decision Tree
- Does Decision Tree require Feature Scaling
Random Forest Classifier And Regresor
- Ensemble Techniques(Boosting And Bagging)
- Working of Random Forest Classifier
- Working of Random Forest Regresor
- Hyperparameter Tuning(Grid Search And RandomSearch)
Theoretical Understanding:
- Tutorial 37:Entropy In Decision Tree https://www.youtube.com/watch?v=1IQOtJ4NI_0
- Tutorial 38:Information Gain https://www.youtube.com/watch?v=FuTRucXB9rA
- Tutorial 39:Gini Impurity https://www.youtube.com/watch?v=5aIFgrrTqOw
- Tutorial 40: Decision Tree For Numerical Features: https://www.youtube.com/watch?v=5O8HvA9pMew
- How To Visualize DT: https://www.youtube.com/watch?v=ot75kOmpYjI
Theoretical Understanding:
- Ensemble technique(Bagging): https://www.youtube.com/watch?v=KIOeZ5cFZ50
- Random forest Classifier And Regressor https://www.youtube.com/watch?v=nxFG5xdpDto
- Construct Decision Tree And working in Random Forest: https://www.youtube.com/watch?v=WQ0iJSbnnZA&t=406s
Important properties of Random Forest Classifiers
Decision Tree—Low Bias And High Variance
Ensemble Bagging(Random Forest Classifier)–Low Bias And Low Variance
1. What Are the Basic Assumption?
There are no such assumptions
2. Advantages
Advantages of Random Forest
Doesn’t Overfit
Favourite algorithm for Kaggle competition
Less Parameter Tuning required
Decision Tree can handle both continuous and categorical variables.
No feature scaling required: No feature scaling (standardization and normalization) required in case of Random Forest as it uses DEcision Tree internally
Suitable for any kind of ML problems
3. Disadvantages
Disadvantages of Random Forest
1.Biased With features having many categories
- Biased in multiclass classification problems towards more frequent classes.
4. Whether Feature Scaling is required?
No
6. Impact of outliers?
Robust to Outliers
Types of Problems it can solve(Supervised)
- Classification
- Regression
Practical Implementation
- https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
- https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
Performance Metrics
Classification
- Confusion Matrix
- Precision,Recall, F1 score
Regression
- R2,Adjusted R2
- MSE,RMSE,MAE
Download the github material from here
