Machine Learning Cheat Sheet
Read NowWhen working with machine learning, it's easy to try them all out without understanding what each model does, and when to use them. In this cheat sheet, you'll find a handy guide describing the most widely used machine learning models, their advantages, disadvantages, and some key usecases.
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Supervised Learning
Supervised learning models are models that map inputs to outputs, and attempt to extrapolate patterns learned in past data on unseen data. Supervised learning models can be either regression models, where we try to predict a continuous variable, like stock prices—or classification models, where we try to predict a binary or multiclass variable, like whether a customer will churn or not. In the section below, we'll explain two popular types of supervised learning models: linear models, and treebased models.
Linear Models
In a nutshell, linear models create a bestfit line to predict unseen data. Linear models imply that outputs are a linear combination of features. In this section, we'll specify commonly used linear models in machine learning, their advantages, and disadvantages.
Algorithm  Description  Applications  Advantages  Disadvantages 
Linear Regression  A simple algorithm that models a linear relationship between inputs and a continuous numerical output variable 



Logistic Regression  A simple algorithm that models a linear relationship between inputs and a categorical output (1 or 0) 



Ridge Regression  Part of the regression family — it penalizes features that have low predictive outcomes by shrinking their coefficients closer to zero. Can be used for classification or regression 



Lasso Regression  Part of the regression family — it penalizes features that have low predictive outcomes by shrinking their coefficients to zero. Can be used for classification or regression 



Treebased models
In a nutshell, treebased models use a series of "ifthen" rules to predict from decision trees. In this section, we'll specify commonly used linear models in machine learning, their advantages, and disadvantages.
Algorithm  Description  Applications  Advantages  Disadvantages 
Decision Tree  Decision Tree models make decision rules on the features to produce predictions. It can be used for classification or regression 



Random Forests  An ensemble learning method that combines the output of multiple decision trees 



Gradient Boosting Regression  Gradient Boosting Regression employs boosting to make predictive models from an ensemble of weak predictive learners 



XGBoost  Gradient Boosting algorithm that is efficient & flexible. Can be used for both classification and regression tasks 



LightGBM Regressor  A gradient boosting framework that is designed to be more efficient than other implementations 



Unsupervised Learning
Unsupervised learning is about discovering general patterns in data. The most popular example is clustering or segmenting customers and users. This type of segmentation is generalizable and can be applied broadly, such as to documents, companies, and genes. Unsupervised learning consists of clustering models, that learn how to group similar data points together, or association algorithms, that group different data points based on predefined rules.
Clustering models
Algorithm  Description  Applications  Advantages  Disadvantages 
KMeans  KMeans is the most widely used clustering approach—it determines K clusters based on euclidean distances 



Hierarchical Clustering  A "bottomup" approach where each data point is treated as its own cluster—and then the closest two clusters are merged together iteratively 



Gaussian Mixture Models  A probabilistic model for modeling normally distributed clusters within a dataset 



Association
Algorithm  Description  Applications  Advantages  Disadvantages 
Apriori Algorithm  Rule based approach that identifies the most frequent itemset in a given dataset where prior knowledge of frequent itemset properties is used 



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