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ML - Part 2 - Classical Models

Logistic Regression:

  • Logistic regression is a supervised learning algorithm used for binary classification tasks.
  • It models the probability of the outcome using a logistic function and separates the classes based on a decision boundary.
  • It is a linear algorithm that can handle both numerical and categorical input features.

 

Decision Trees:

  • Decision trees are supervised learning algorithms used for classification and regression tasks.
  • They partition the data based on feature conditions to create a tree-like model of decisions and their consequences.
  • Decision trees are interpretable and can handle both numerical and categorical features.

 

Random Forest:

  • Random Forest is an ensemble learning algorithm that combines multiple decision trees.
  • It builds a collection of decision trees and makes predictions by aggregating the individual tree’s predictions.
  • Random Forest is robust against overfitting and can handle high-dimensional data.

 

Support Vector Machines (SVM):

  • SVM is a supervised learning algorithm used for classification and regression tasks.
  • It separates classes by finding the hyperplane that maximizes the margin between them.
  • SVM can handle both linear and non-linear decision boundaries using kernel functions.

 

Naive Bayes:

  • Naive Bayes is a probabilistic supervised learning algorithm used for classification tasks.
  • It is based on Bayes’ theorem and assumes independence between features given the class.
  • Naive Bayes is efficient, particularly for high-dimensional data, but it makes a strong assumption of feature independence.

 

Regularization:

  • Regularization is a technique used to prevent overfitting in machine learning models.
  • It adds a penalty term to the model’s objective function, discouraging complex or extreme parameter values.
  • Common regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.

 

DBSCAN (Density-Based Spatial Clustering of Applications with Noise):

  • DBSCAN is an unsupervised learning algorithm used for density-based clustering.
  • It groups together data points that are close to each other and separates regions of lower density.
  • DBSCAN can identify clusters of arbitrary shape and handle noise points.

 

Gaussian Mixture Model (GMM):

  • GMM is a probabilistic unsupervised learning algorithm used for clustering and density estimation.
  • It assumes that the data is generated from a mixture of Gaussian distributions.
  • GMM assigns probabilities to each data point, allowing for soft clustering and handling overlapping clusters.
Machine_Learning
Machine Learning – PART 2 – Classical Models

ML