Regression Techniques Overview

What is linear regression used for?

• To calculate the mean of a dataset.

• To predict numerical outcomes

• To create visualizations of data.

• To perform hypothesis testing on samples.

How does decision tree regression work?

• It predicts values by averaging all feature values directly.

• It uses linear equations to model relationships between features.

• It creates a single branch that leads to a final prediction.

• It predicts values by creating a model that splits data into branches based on feature values to get the average target values in leaf nodes.

What is the purpose of the cost function in linear regression?

• To measure the error between predicted and actual values

• To visualize the data points on a graph.

• To determine the best features for the model.

• To calculate the average of the predicted values.

How do you prevent overfitting in decision tree regression?

• Train the model longer without validation

• Increase the number of features

• Use pruning, limit tree depth, and set minimum samples per split

• Use a larger dataset

What activation functions are commonly used in neural networks?

• Polynomial

• Kernel

• Sigmoid and Tanh

• Linear

How can you evaluate the performance of a regression model?

• Dunn Index

• Use MAE, MSE and RMSE

• AUC/ROC

• Accuracy, Precision and Recall

What is the difference between supervised and unsupervised learning?

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

• Supervised learning requires no data for training, while unsupervised learning requires labeled data.

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

• Supervised learning can only be applied to images, while unsupervised learning can only be applied to text.