Sklearn Algorithms Cheat Sheet with Examples

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.

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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
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