Exploring Machine Learning Concepts

What is the primary goal of machine learning?

• To enable computers to learn from data and make predictions or decisions.

• To store large amounts of data without analysis.

• To replace human intelligence entirely.

• To create a perfect algorithm that never makes mistakes.

Define supervised learning.

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

• Unsupervised learning involves training models without labeled data.

• Supervised learning is a method that only uses unstructured data.

• Supervised learning is a technique for clustering data points without labels.

What is the difference between classification and regression?

• Classification is used for time series; regression is for image analysis.

• Classification requires more data than regression.

• Classification predicts categories; regression predicts continuous values.

• Classification predicts numerical values; regression predicts categories.

Explain the concept of overfitting in machine learning.

• Overfitting happens when a model is trained on too little data, leading to generalization issues.

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

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

• Overfitting is when a model learns the training data too well, leading to poor performance on new data.

What is a training set in machine learning?

• A training set is a dataset used to train a machine learning model.

• A training set is a type of machine learning model.

• A training set is a dataset used for testing a machine learning model.

• A training set is a collection of algorithms for machine learning.

Describe the role of features in a machine learning model.

• Features are irrelevant to model performance.

• Features only serve as output labels.

• Features are the final predictions made by the model.

• Features are essential inputs that help machine learning models learn patterns and make predictions.

What is the purpose of a validation set?

• To store the training data for future use.

• To evaluate the final model performance on unseen data.

• The purpose of a validation set is to tune model hyperparameters and prevent overfitting.

• To increase the size of the training dataset.

Name a common algorithm used for classification tasks.

• Decision Tree

• Support Vector Machine

• K-Means Clustering

• Linear Regression

What does the term 'hyperparameter' refer to?

• A parameter set before the learning process that controls the training of a machine learning model.

• A parameter that is automatically adjusted during training.

• A type of data preprocessing technique used before training.

• A feature of the model that is learned from the data.

Explain the concept of cross-validation.

• Cross-validation is a technique for visualizing data distributions.

• Cross-validation is used to eliminate all errors in a statistical model.

• Cross-validation is a method for increasing the size of a dataset.

• Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.