The primary focus of machine learning is to enable computers to learn from data and make predictions or decisions.

To focus solely on hardware improvements for faster processing.

To replace human decision-making entirely without data.

To create static algorithms that do not adapt over time.

Transfer learning

Deep learning

Reinforcement learning

Supervised learning, Unsupervised learning

Supervised learning is a type of reinforcement learning.

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

Unsupervised learning uses labeled data to train models.

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

Unsupervised learning is a machine learning approach that analyzes and identifies patterns in unlabeled data.

Unsupervised learning is a technique that only works with structured data.

Unsupervised learning is a method for supervised classification tasks.

Unsupervised learning requires labeled data for training.

Reinforcement learning is a method for supervised learning using labeled data.

Reinforcement learning is a technique for clustering data into groups.

Reinforcement learning is a type of machine learning that focuses solely on data analysis.

Reinforcement learning is a type of machine learning focused on training agents to make decisions through trial and error to maximize rewards.

Regression is a technique for reducing the dimensionality of data.

Regression is used to classify categorical outcomes.

Regression is a supervised learning method used to predict continuous outcomes.

Regression is an unsupervised learning method for clustering data.

R-squared (R2)

decision tree

Mean Squared Error (MSE)

algorithm

To determine the best features for a model.

To visualize the data distribution.

To optimize the training time of models.

The purpose of classification metrics is to evaluate the performance of classification models.

Accuracy is the proportion of true results (both true positives and true negatives) among the total number of cases examined.

Accuracy is the number of true positives only.

Accuracy is the ratio of false results to total cases.

Accuracy measures the speed of classification algorithms.

Precision is the ratio of true positives to the total number of predictions.

Precision measures the overall accuracy of the classification model.

Precision is the number of true positives divided by the number of true negatives.

Precision is the ratio of true positives to the sum of true positives and false positives.

Recall is the ratio of true negatives to the total actual negatives.

Recall is the ratio of true positives to the total actual positives, indicating the model's ability to find all relevant cases.

Recall measures the overall accuracy of the model.

Recall is the percentage of false positives in a model.

The F1 score is a metric used for regression analysis.

The F1 score measures the overall accuracy of a model.

The F1 score represents the balance between precision and recall in a classification model.

The F1 score indicates the number of true positives only.

Regression requires labeled data; classification does not need any data.

Regression deals with binary outcomes; classification handles multiple variables.

Regression predicts continuous outcomes; classification predicts categorical outcomes.

Regression is used for time series analysis; classification is for clustering.

A confusion matrix is a table that summarizes the performance of a classification model by comparing predicted and actual classifications.

A confusion matrix is a method for data normalization.

A confusion matrix is a statistical test for hypothesis testing.

A confusion matrix is a type of neural network architecture.