Introduction to Machine Learning

What is supervised learning?

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a machine learning approach that uses labeled data to train models.

• Supervised learning is a type of reinforcement learning.

• Supervised learning is a method that requires no data for training.

What is the main difference between classification and regression?

• Classification deals with categorical outcomes, whereas regression deals with continuous outcomes.

• Classification requires more data than regression to be effective.

• Regression is used for binary outcomes, while classification is for continuous outcomes.

• Classification predicts numerical values, while regression predicts categories.

What does overfitting mean in machine learning?

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

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

• Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

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

What is underfitting and how does it occur?

• Underfitting happens when there is too much data for the model to process.

• Underfitting is when a model is too simple to learn the underlying patterns in the data.

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

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

Name one common evaluation metric for classification models.

• F1 Score

• Accuracy

• Recall

• Precision

What is a confusion matrix used for?

• To calculate the mean and variance of a dataset.

• To visualize the distribution of data points in a dataset.

• To perform regression analysis on numerical data.

• A confusion matrix is used for evaluating the performance of a classification model.

What is feature engineering in machine learning?

• Feature engineering is the evaluation of model performance after training.

• Feature engineering is the process of collecting data from various sources.

• Feature engineering refers to the algorithm used for training models.

• Feature engineering is the process of creating and selecting features to improve model performance in machine learning.

Why is feature scaling important?

• Feature scaling can reduce the accuracy of the model.

• Feature scaling is important to ensure that all features contribute equally to model training and improve algorithm performance.

• Feature scaling is only necessary for categorical data.

• Feature scaling is used to increase the number of features in the dataset.

What is the purpose of cross-validation?

• To reduce the complexity of the model.

• To eliminate the need for data preprocessing.

• To increase the size of the training dataset.

• The purpose of cross-validation is to evaluate the performance and generalizability of a model.

How can you prevent overfitting in a model?

• Increase the model complexity

• Ignore validation metrics

• Use a single training dataset

• Use techniques like regularization, cross-validation, and dropout.