5 Machine Learning Algorithms Commonly Used in Python
Machine learning algorithms are pivotal in extracting knowledge from data and generating predictions. Python, a popular programming language, offers various widely used learning algorithms that address multiple challenges. These algorithms extract patterns and correlations from data, enabling computers to reason and predict future outcomes. Here’s a summary of five renowned machine-learning algorithms used in Python:
| Algorithm | Description |
|---|---|
| Naive Bayes | A classification algorithm based on the Bayes theorem. It assumes features of the same class are independent of features from other classes. It performs well with large datasets. |
| Random Forest | An ensemble learning method is used for classification, regression, and other tasks. It constructs multiple decision trees during training and chooses the class with the most trees. |
| Linear Regression | Predicts outcomes based on independent variables, establishing a linear relationship between them. It illustrates how the value of independent variables impacts the dependent variable. |
| Back-propagation | A supervised learning algorithm is used for classification and regression. Adjusts input signal weights to produce desired output signals. It uses gradient descent or delta rule techniques to minimize error functions. |
| KNN (K-nearest Neighbours) | Classifies data points by examining the labels of surrounding data points. Used for both classification and regression tasks. Identifies patterns in data and detects anomalies. |
The original article is “5 Machine Learning Algorithms Commonly Used in Python.“