Interview Questions and Answers

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 involves iteratively training models on the data, with each sub...read more

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 miss relevant relations between features and target outputs...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

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 score.

• Feature selection and engineering can improve classifica...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 feature importance.

• Example: predicting customer churn, fraud det...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