Section 1: Regression Algorithms

Regression algorithms fall under supervised learning and are used to identify relationships between variables, helping us understand how independent variables impact a dependent one.

Think of regression analysis as a mathematical equation. For instance, in the equation (y = 2x + z), (y) is the dependent variable while (x) and (z) are independent variables. Regression determines the extent to which (x) and (z) influence (y).

Various types of regression algorithms exist, including:

• Linear Regression: A straightforward technique that models the relationship between dependent and independent variables using a linear approach.
• Logistic Regression: Used for binary dependent variables, it’s commonly employed to analyze categorical data.
• Ridge Regression: This method addresses issues of complexity in regression models by adjusting the coefficients.
• Lasso Regression: This technique selects and regularizes variables, enhancing model performance.
• Polynomial Regression: This algorithm fits non-linear data by using curves instead of straight lines for predictions.

Section 2: Classification Algorithms

Classification is the process of organizing items into categories based on a pre-categorized training dataset. This is a prime example of supervised learning.

Classification algorithms utilize the labeled training data to estimate the likelihood that new items belong to certain categories. A common application is in filtering emails as spam or not spam.

Key classification algorithms include:

• K-Nearest Neighbors (KNN): This algorithm identifies the (k) closest data points in a dataset.
• Decision Trees: Visualized as flowcharts, they classify data points by breaking them down into binary categories.
• Naive Bayes: It calculates the probability of an item belonging to a category using conditional probabilities.
• Support Vector Machine (SVM): This algorithm classifies data based on its polarity, which can extend beyond simple X/Y predictions.

Section 3: Ensembling Techniques

Ensembling algorithms are a type of supervised learning that combines the predictions of multiple algorithms to enhance accuracy. The methods of combination can include voting or averaging results.

The three primary types of ensembling are:

• Bagging: Algorithms run in parallel on different subsets of training data, with results determined through voting.
• Boosting: Algorithms are executed sequentially, with overall results selected through weighted voting.
• Stacking: This involves multiple levels, where a base level consists of various algorithms, and a meta-algorithm operates on the results of the base level.

Section 4: Clustering Algorithms

Clustering algorithms are unsupervised methods that group data points based on their similarities. Data points within the same cluster share greater similarity than those in different clusters.

Four common clustering algorithms include:

• Centroid-based Clustering: This organizes data into clusters based on initial conditions; the most recognized example is the k-means algorithm.
• Density-based Clustering: This connects high-density regions into clusters, yielding arbitrary shapes.
• Distribution-based Clustering: This method assumes data is composed of probability distributions and clusters it accordingly.
• Hierarchical Clustering: This creates a tree-like structure of clusters that can be adjusted by cutting the tree at various levels.

Section 5: Association Algorithms

Association algorithms are unsupervised methods used to uncover patterns of items that frequently co-occur in datasets. They are particularly useful in market-basket analysis.

The Apriori Algorithm is a widely used method that identifies frequent itemsets and formulates association rules based on those sets. For example, if a consumer purchases milk and bread, they are likely to buy eggs as well, based on patterns from previous transactions.