Feature scaling ensures that all input variables are on a similar scale, improving the convergence speed of gradient-based optimization algorithms.
Naïve Bayes is widely used for spam filtering due to its effectiveness in probabilistic classification based on word frequency.
Principal Component Analysis (PCA) is a popular dimensionality reduction technique that transforms high-dimensional data into fewer dimensions while retaining variance.
K-Means Clustering is a widely used unsupervised learning algorithm for grouping similar data points based on their features.
Mean Squared Error (MSE) measures the average squared difference between actual and predicted values, making it a key metric for regression models.
Cross-validation helps assess a model's generalizability by partitioning the dataset into training and testing sets multiple times.
