Choosing the right ML model can make or break your project💡
This document lists down the pros and cons of each Machine Learning model.
The models covered in this thread are:
🧵:
This document lists down the pros and cons of each Machine Learning model.
The models covered in this thread are:
🧵:
Linear Regression: A foundational model that predicts a continuous outcome variable based on one or more predictor variables.
Logistic Regression: Used for binary classification tasks. It estimates the probability that a given instance belongs to a particular category
Decision Tree: A flowchart-like structure where each node represents a feature, each branch a decision rule, and each leaf a class label
K-Nearest Neighbour (KNN): Classifies a data point based on how its neighbors are classified
K-Means: An unsupervised clustering algorithm that groups data into 'K' number of clusters
Support Vector Machine (SVM): Finds the hyperplane that best divides a dataset into classes
Principal Component Analysis (PCA): A dimensionality reduction technique that transforms data into a new coordinate system
Naive Bayes: Based on Bayes' theorem, it's particularly suitable for high-dimensional datasets
Artificial Neural Networks (ANN): Inspired by the human brain, it consists of interconnected neurons
AdaBoost: An ensemble method that adjusts weights of misclassified data points
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