Skip to content
Related Articles

Related Articles

Improve Article

Flowchart for basic Machine Learning models

  • Last Updated : 05 Sep, 2020
Geek Week

Machine learning tasks have been divided into three categories, depending upon the feedback available:

  1. Supervised Learning: These are human builds models based on input and output.
  2. 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.
  3. 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:

AlgorithmSupervisedUnsupervisedReinforcement 
Linear100
Logistic100
K-Means110
Anomaly Detection110
Neural Net111
KNN100
Decision Tee100
Random Forest100
SVM100
Naive Bayes100
 

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

Basic RegressionLinearlinear_model.LinearRegression()Lots of numerical data
Logisticlinear_model.LogisticRegression()Target variable is categorical
Cluster AnalysisK-Meanscluster.KMeans()Similar datum into groups based on centroids
Anomaly Detectioncovariance.EllipticalEnvelope()Finding outliers through grouping
ClassificationNeural Netneural_network.MLPClassifier()Complex relationships. Prone to over fitting.
K-NNneighbors.KNeighborsClassifier()Group membership based on proximity
Decision Teetree.DecisionTreeClassifier()If/then/else. Non-contiguous data. Can also be regression.
Random Forestensemble.RandomForestClassifier()Find best split randomly. Can also be regression
SVM

svm.SVC() 

svm.LinearSVC()

Maximum margin classifier. Fundamental. Data Science algorithm
Naive BayesGaussianNB() MultinominalNB() BernoulliNB()Updating knowledge step by step with new info
Feature ReductionT-DISTRIB Stochastic NEIB Embeddingmanifold.TSNE()Visual high dimensional data. Convert similarity to joint probabilities
Principle Component Analysisdecomposition.PCA()Distill feature space into components that describe the greatest variance
Canonical Correlation Analysisdecomposition.CCA()Making sense of cross-correlation matrices
Linear Discriminant Analysislda.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.

Attention reader! Don’t stop learning now. Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready.

My Personal Notes arrow_drop_up
Recommended Articles
Page :