What are the differences between various machine learning algorithms?

What are the differences between various machine learning algorithms?

There are several types of machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Supervised algorithms like linear regression and decision trees are used for labeled data, while unsupervised algorithms like k-means clustering are used for unlabeled data.

Can you explain how decision trees work?

Decision trees work by splitting the data into subsets based on the value of input features. Each node represents a feature, and each branch represents a decision rule, leading to a final output at the leaves.

What are the advantages of using random forests over decision trees?

Random forests reduce overfitting by averaging multiple decision trees, which improves accuracy and robustness. They also handle missing values better.

Which algorithm is best for image recognition?

Convolutional Neural Networks (CNNs) are typically the best choice for image recognition tasks due to their ability to capture spatial hierarchies in images.