Machine Learning

The dataset used to train a machine learning model. It consists of input features and corresponding labels (in supervised learning) or only input features (in unsupervised learning).

Features are the input variables or attributes used to make predictions, while labels are the target variables that the model aims to predict.

In supervised learning, the model learns from labeled data. It is trained on input-output pairs and learns to map inputs to corresponding outputs. Common supervised learning tasks include classification (predicting labels) and regression (predicting continuous values).

In unsupervised learning, the model learns from unlabeled data. It aims to discover hidden patterns or structures in the data, such as clustering similar data points or dimensionality reduction.

The process of selecting, transforming, or creating new features from raw data to improve the performance of machine learning models.

Assessing the performance of a machine learning model using evaluation metrics such as