Sklearn Algorithms Cheat Sheet with Examples

sklearn algorithms cheat sheet

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 TypeProblem TypeAlgorithmsSklearn ClassExample Use Case
Supervised LearningClassificationLogistic Regressionsklearn.linear_model.LogisticRegressionPredicting customer churn in telecom industry
Supervised LearningClassificationDecision Treessklearn.tree.DecisionTreeClassifierIdentifying the best advertising channels for a product launch based on historical sales data
Supervised LearningClassificationRandom Forestssklearn.ensemble.RandomForestClassifierIdentifying spam emails based on message content, sender information, and email metadata.
Supervised LearningClassificationKNN (K-Nearest Neighbors)sklearn.neighbors.KNeighborsClassifierClassifying handwritten digits using MNIST dataset
Supervised LearningClassificationSVM (Support Vector Machines)sklearn.svm.SVCClassifying images for object recognition, such as detecting faces in photos or identifying cancer cells in medical images
Supervised LearningClassificationNaive Bayessklearn.naive_bayes.GaussianNBIdentifying spam emails from normal ones
Supervised LearningClassificationNeural Networkssklearn.neural_network.MLPClassifierImage classification using CIFAR-10 dataset
Supervised LearningRegressionLinear Regressionsklearn.linear_model.LinearRegressionPredicting the price of a house based on its characteristics
Supervised LearningRegressionPolynomial Regressionsklearn.preprocessing.PolynomialFeaturesModeling non-linear relationships in data
Supervised LearningRegressionRidge Regressionsklearn.linear_model.RidgeDealing with multicollinearity in linear regression
Supervised LearningRegressionLasso Regressionsklearn.linear_model.LassoFeature selection in linear regression
Supervised LearningRegressionElasticNet Regressionsklearn.linear_model.ElasticNetCombining L1 and L2 regularization in linear regression
Supervised LearningRegressionRandom Forest Regressionsklearn.ensemble.RandomForestRegressorPredicting payment collection date in AR
Supervised LearningRegressionSVM (Support Vector Machines)sklearn.svm.SVRPredicting the stock prices based on historical data
Supervised LearningRegressionNeural Networkssklearn.neural_network.MLPRegressorPredicting the energy output of a power plant
Unsupervised LearningClusteringK-Means Clusteringsklearn.cluster.KMeansGrouping customers based on their purchasing behavior
Unsupervised LearningClusteringHierarchical Clusteringsklearn.cluster.AgglomerativeClusteringCreating a phylogenetic tree based on DNA sequences
Unsupervised LearningClusteringDBSCANsklearn.cluster.DBSCANIdentifying dense regions in a spatial dataset
Unsupervised LearningClusteringGaussian Mixture Modelssklearn.mixture.GaussianMixtureIdentifying subpopulations in a dataset
Unsupervised LearningClusteringMean-Shift Clusteringsklearn.cluster.MeanShiftImage segmentation in computer vision
Unsupervised LearningClusteringSpectral Clusteringsklearn.cluster.SpectralClusteringCommunity detection in social networks

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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.

Ajitesh Kumar
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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. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,
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