Exploring Supervised Learning Concepts

What is the primary goal of regression analysis?

• To analyze time series data only.

• To determine causation between variables.

• To summarize data in a report.

• To model relationships and make predictions.

Which of the following is a common regression technique?

• Logistic Regression

• Linear Regression

• Decision Trees

• Support Vector Machines

What is the difference between linear and logistic regression?

• Linear regression is used for time series analysis, while logistic regression is used for forecasting.

• Linear regression predicts probabilities, while logistic regression predicts continuous values.

• Linear regression predicts continuous values, while logistic regression predicts probabilities for binary outcomes.

• Linear regression can only handle categorical variables, while logistic regression handles continuous variables.

What is a classification technique used for predicting categorical outcomes?

• K-Means Clustering

• Logistic Regression

• Linear Regression

• Decision Tree

Which algorithm is commonly used for binary classification?

• K-Means Clustering

• Logistic Regression

• Support Vector Machine

• Decision Tree

What does the term 'overfitting' refer to in machine learning?

• Overfitting is when a model performs equally well on both training and new data.

• Overfitting refers to a model that is trained on too little data, leading to high bias.

• Overfitting refers to a model that is too complex and learns the training data too well, leading to poor performance on new data.

• Overfitting occurs when a model is too simple and fails to capture the underlying patterns in the data.

How can overfitting be mitigated?

• Increase model complexity

• Reduce training data

• Use regularization, cross-validation, and increase training data.

• Use a single validation set

What is 'underfitting' in the context of model training?

• Underfitting happens when a model is trained on too much data.

• Underfitting is when a model fails to learn the underlying structure of the data, leading to poor performance.

• Underfitting is when a model perfectly captures the data patterns.

• Underfitting occurs when a model is too complex and overfits the data.

What is the purpose of evaluation metrics in supervised learning?

• To predict future data points accurately.

• To increase the training dataset size.

• To simplify the model architecture.

• To assess model performance and guide improvements.

Which metric is commonly used to evaluate regression models?

• R-squared

• Accuracy

• Precision

• Mean Squared Error (MSE)