The Sklearn library, short for Scikit-learn, is one of the most popular and widely-used libraries for machine learning in Python. It offers a comprehensive set of tools for data analysis, preprocessing, model selection, and evaluation. As a beginner data scientist, it can be overwhelming to navigate the various algorithms and functions within Sklearn. This is where the Sklearn Algorithms Cheat Sheet comes in handy. This cheat sheet provides a quick reference guide for beginners to easily understand and select the appropriate algorithm for their specific task.
In this cheat sheet, I have compiled a list of common supervised and unsupervised learning algorithms, along with their Sklearn classes and example use cases. This makes it easier for beginners to get started with machine learning tasks and understand which algorithms are suitable for their data.
The Sklearn Algorithms Cheat Sheet can serves as a starting point for beginners to get comfortable with the library and start experimenting with machine learning models. As users gain more experience, they can expand their knowledge and explore additional algorithms and techniques. It is worth noting that this cheat sheet is not exhaustive and will continue to evolve over time with more algorithms and example use cases added.
| Task Type | Problem Type | Algorithms | Sklearn Class | Example Use Case |
|---|---|---|---|---|
| Supervised Learning | Classification | Logistic Regression | sklearn.linear_model.LogisticRegression | Predicting customer churn in telecom industry |
| Supervised Learning | Classification | Decision Trees | sklearn.tree.DecisionTreeClassifier | Identifying the best advertising channels for a product launch based on historical sales data |
| Supervised Learning | Classification | Random Forests | sklearn.ensemble.RandomForestClassifier | Identifying spam emails based on message content, sender information, and email metadata. |
| Supervised Learning | Classification | KNN (K-Nearest Neighbors) | sklearn.neighbors.KNeighborsClassifier | Classifying handwritten digits using MNIST dataset |
| Supervised Learning | Classification | SVM (Support Vector Machines) | sklearn.svm.SVC | Classifying images for object recognition, such as detecting faces in photos or identifying cancer cells in medical images |
| Supervised Learning | Classification | Naive Bayes | sklearn.naive_bayes.GaussianNB | Identifying spam emails from normal ones |
| Supervised Learning | Classification | Neural Networks | sklearn.neural_network.MLPClassifier | Image classification using CIFAR-10 dataset |
| Supervised Learning | Regression | Linear Regression | sklearn.linear_model.LinearRegression | Predicting the price of a house based on its characteristics |
| Supervised Learning | Regression | Polynomial Regression | sklearn.preprocessing.PolynomialFeatures | Modeling non-linear relationships in data |
| Supervised Learning | Regression | Ridge Regression | sklearn.linear_model.Ridge | Dealing with multicollinearity in linear regression |
| Supervised Learning | Regression | Lasso Regression | sklearn.linear_model.Lasso | Feature selection in linear regression |
| Supervised Learning | Regression | ElasticNet Regression | sklearn.linear_model.ElasticNet | Combining L1 and L2 regularization in linear regression |
| Supervised Learning | Regression | Random Forest Regression | sklearn.ensemble.RandomForestRegressor | Predicting payment collection date in AR |
| Supervised Learning | Regression | SVM (Support Vector Machines) | sklearn.svm.SVR | Predicting the stock prices based on historical data |
| Supervised Learning | Regression | Neural Networks | sklearn.neural_network.MLPRegressor | Predicting the energy output of a power plant |
| Unsupervised Learning | Clustering | K-Means Clustering | sklearn.cluster.KMeans | Grouping customers based on their purchasing behavior |
| Unsupervised Learning | Clustering | Hierarchical Clustering | sklearn.cluster.AgglomerativeClustering | Creating a phylogenetic tree based on DNA sequences |
| Unsupervised Learning | Clustering | DBSCAN | sklearn.cluster.DBSCAN | Identifying dense regions in a spatial dataset |
| Unsupervised Learning | Clustering | Gaussian Mixture Models | sklearn.mixture.GaussianMixture | Identifying subpopulations in a dataset |
| Unsupervised Learning | Clustering | Mean-Shift Clustering | sklearn.cluster.MeanShift | Image segmentation in computer vision |
| Unsupervised Learning | Clustering | Spectral Clustering | sklearn.cluster.SpectralClustering | Community detection in social networks |
Conclusion
The Sklearn Algorithms Cheat Sheet is an essential resource for any data scientist, especially for beginners who are just starting with Python programming and machine learning tasks. By providing a concise and easy-to-understand reference guide to common Sklearn algorithms, this cheat sheet can save time and effort when selecting the right algorithm for a specific task. As you gain more experience, you can continue to refer to the cheat sheet and expand your knowledge with more advanced algorithms and techniques. I hope that this cheat sheet serves as a valuable resource for data scientists and helps to simplify their machine learning workflow.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com
- MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use? - April 22, 2024
- Gradient Descent in Machine Learning: Python Examples - April 22, 2024
- Loss Function vs Cost Function vs Objective Function: Examples - April 19, 2024
Leave a Reply