Great Learning Interview Questions, Process, and Tips for Experienced

Ensemble techniques combine multiple models to improve prediction accuracy.

• Ensemble techniques can be used with various types of models, such as decision trees, neural networks, and support vector machines.

• Common ensemble techniques include bagging, boosting, and stacking.

• Bagging involves training multiple models on different subsets of the data and combining their predictions through averaging or voting.

• Boosting invol...read more

Ensemble techniques combine multiple models to improve prediction accuracy.

• Bagging: Bootstrap Aggregating

• Boosting: AdaBoost, Gradient Boosting

• Stacking: Meta-model combines predictions of base models

• Voting: Combining predictions of multiple models by majority voting

Bagging is a technique used in machine learning to improve the stability and accuracy of a model by combining multiple models.

• Bagging stands for Bootstrap Aggregating.

• It involves creating multiple subsets of the original dataset by randomly sampling with replacement.

• Each subset is used to train a separate model, and the final prediction is the average of all the predictions made by each model.

• Bagging reduces overfittin...read more

Boosting is an ensemble learning technique that combines multiple weak models to create a strong model.

• Boosting iteratively trains weak models on different subsets of data

• Each subsequent model focuses on the misclassified data points of the previous model

• Final prediction is made by weighted combination of all models

• Examples include AdaBoost, Gradient Boosting, XGBoost

Bias is error due to erroneous assumptions in the learning algorithm. Variance is error due to sensitivity to small fluctuations in the training set.

• Bias is the difference between the expected prediction of the model and the correct value that we are trying to predict.

• Variance is the variability of model prediction for a given data point or a value which tells us spread of our data.

• High bias can cause an algorithm to m...read more

Classification techniques are used to categorize data into different classes or groups based on certain features or attributes.

• Common classification techniques include decision trees, logistic regression, k-nearest neighbors, and support vector machines.

• Classification can be binary (two classes) or multi-class (more than two classes).

• Evaluation metrics for classification include accuracy, precision, recall, and F1 scor...read more

Random forest is an ensemble learning method for classification, regression and other tasks.

• Random forest builds multiple decision trees and combines their predictions to improve accuracy.

• It uses bagging technique to create multiple subsets of data and features for each tree.

• Random forest reduces overfitting and is robust to outliers and missing values.

• It can handle high-dimensional data and is easy to interpret featur...read more