Exploring Supervised Learning Concepts

What is regression analysis used for in supervised learning?

• To classify categorical outcomes based on input features.

• To reduce the dimensionality of the input features.

• To predict continuous outcomes based on input features.

• To analyze the relationship between two categorical variables.

Name two common classification techniques.

• Decision Trees, Support Vector Machines

• Linear Regression

• K-Nearest Neighbors

• Neural Networks

Explain the concepts of overfitting and underfitting.

• Overfitting occurs when a model is too simple and fails to learn from the training data.

• Overfitting and underfitting are terms used to describe the speed of a model's training process.

• Overfitting is when a model is too complex and learns noise from the training data, while underfitting is when a model is too simple to capture the data's patterns.

• Underfitting is when a model is overly complex and captures too much noise from the data.

What is the purpose of evaluation metrics in machine learning?

• To eliminate the need for data preprocessing.

• To assess and compare the performance of machine learning models.

• To provide a random selection of model parameters.

• To increase the complexity of machine learning models.

How do you split data into training and testing sets?

• Combine training and testing sets into one dataset.

• Use only the first half of the dataset for training.

• Split the dataset into equal parts without shuffling.

• Randomly shuffle the dataset and split it into training and testing sets based on a defined ratio.

What is feature selection and why is it important?

• Feature selection is the process of removing all features from a model.

• Feature selection is the process of adding more features to a model.

• Feature selection is the process of selecting relevant features for model building, important for improving performance and interpretability.

• Feature selection is only relevant for deep learning models.

Describe the difference between linear and logistic regression.

• Linear regression predicts continuous outcomes; logistic regression predicts binary outcomes.

• Linear regression can only handle categorical data; logistic regression handles continuous data.

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

• Linear regression predicts probabilities; logistic regression predicts numerical values.

What are precision and recall in the context of classification?

• Precision measures the total number of predictions made; Recall measures the total number of actual instances.

• Precision is the ratio of false positives to true positives; Recall is the ratio of false negatives to true positives.

• Precision is the percentage of correct predictions; Recall is the percentage of incorrect predictions.

• Precision is the ratio of true positives to the sum of true and false positives; Recall is the ratio of true positives to the sum of true positives and false negatives.

How can cross-validation help in model evaluation?

• Cross-validation guarantees a perfect model fit.

• Cross-validation eliminates the need for a training set.

• Cross-validation provides a robust method for assessing model performance and generalization.

• Cross-validation is only useful for linear models.

What role does the training data play in supervised learning?

• The training data serves as the foundation for the model to learn from labeled examples.

• The training data is irrelevant to the learning process.

• The training data only contains unlabelled examples.

• The training data is used to validate the model's performance.