Flowchart for basic Machine Learning models
Machine learning tasks have been divided into three categories, depending upon the feedback available:
- Supervised Learning: These are human builds models based on input and output.
- Unsupervised Learning: These are models that depend on human input. No labels are given to the learning algorithm, the model has to figure out the structure by itself.
- Reinforcement learning: These are the models that are feed with human inputs. No labels are given to the learning algorithm. The algorithm learns by the rewards and penalties given.
The algorithms that can be used for each of the categories are:
Algorithm Supervised Unsupervised Reinforcement Linear 1 0 0 Logistic 1 0 0 K-Means 1 1 0 Anomaly Detection 1 1 0 Neural Net 1 1 1 KNN 1 0 0 Decision Tee 1 0 0 Random Forest 1 0 0 SVM 1 0 0 Naive Bayes 1 0 0
The machine learning functions and uses for various tasks are given in the below table. To know more about the Algorithms click here. Category Algorithm Function Use svm.SVC() svm.LinearSVC()
Basic Regression Linear linear_model.LinearRegression() Lots of numerical data Logistic linear_model.LogisticRegression() Target variable is categorical Cluster Analysis K-Means cluster.KMeans() Similar datum into groups based on centroids Anomaly Detection covariance.EllipticalEnvelope() Finding outliers through grouping Classification Neural Net neural_network.MLPClassifier() Complex relationships. Prone to over fitting. K-NN neighbors.KNeighborsClassifier() Group membership based on proximity Decision Tee tree.DecisionTreeClassifier() If/then/else. Non-contiguous data. Can also be regression. Random Forest ensemble.RandomForestClassifier() Find best split randomly. Can also be regression SVM Maximum margin classifier. Fundamental. Data Science algorithm Naive Bayes GaussianNB() MultinominalNB() BernoulliNB() Updating knowledge step by step with new info Feature Reduction T-DISTRIB Stochastic NEIB Embedding manifold.TSNE() Visual high dimensional data. Convert similarity to joint probabilities Principle Component Analysis decomposition.PCA() Distill feature space into components that describe the greatest variance Canonical Correlation Analysis decomposition.CCA() Making sense of cross-correlation matrices Linear Discriminant Analysis lda.LDA() Linear combination of features that separates classes
The flowchart given below will help you give a rough guide of each estimator that will help to know more about the task and the ways to solve it using various ML techniques.
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