scikit-learn models ideal use cases
A quick summary of the most common scikit-learn models and their ideal use cases:
- Linear Regression: used for regression tasks where there is a linear relationship between the features and target variable.
- Logistic Regression: used for binary classification tasks. Logistic regression can also be used for multi-class classification. One common way to use logistic regression for multi-class classification is to train multiple binary logistic regression classifiers, each of which predicts the probability of one class versus all other classes. This is known as the One-vs-Rest (OvR) or One-vs-All (OvA) approach. Another approach is to use the multinomial logistic regression, also known as softmax regression, which directly predicts the probabilities of each class. Both approaches can be implemented in scikit-learn's LogisticRegression model by setting the multi_class parameter to either "ovr" or "multinomial", respectively.
- Decision Tree: used for both classification and regression tasks, especially when the data has a nonlinear relationship between the features and target variable.
- Random Forest: used for both classification and regression tasks, especially when the data has complex relationships between the features and target variable.
- Support Vector Machine: used for both classification and regression tasks, especially when there is a clear separation between classes or a nonlinear relationship between the features and target variable.
- Naive Bayes: used for classification tasks, especially when the data has a large number of features and a relatively small amount of training data.
- K-Nearest Neighbors: used for both classification and regression tasks, especially when there is no clear separation between classes or a nonlinear relationship between the features and target variable.
- Gradient Boosting: used for both classification and regression tasks, especially when there are many weak learners (e.g. decision trees) that need to be combined to form a strong learner.
- Neural Networks: used for both classification and regression tasks, especially when there are complex relationships between the features and target variable.
Examples of real-world applications for the ML algorithms mentioned above:
- Linear Regression: Predicting housing prices, stock prices, and sales forecasting.
- Logistic Regression: Predicting whether a customer will churn, determining the likelihood of a customer responding to a marketing campaign, fraud detection, and sentiment analysis.
- Decision Trees: Credit risk analysis, loan eligibility, determining whether a customer will default on a loan, and product recommendation systems.
- Random Forest: Predicting customer churn, credit scoring, and predicting customer lifetime value.
- Support Vector Machines (SVM): Image classification, detecting spam emails, and predicting stock prices.
- Naive Bayes: Spam filtering, sentiment analysis, and text classification.
- k-Nearest Neighbors (KNN): Recommender systems, predicting customer behavior, and image recognition.
- Gradient Boosting: Fraud detection, predicting customer churn, and text classification.
These examples are non-exhaustive, and the use cases for each model can also vary depending on the problem and data being used. There may be situations where one model performs better than another even if it's not listed as the ideal use case for it.