Supervised and Unsupervised Algorithms in scikit-learn


 supervised and unsupervised algorithm functions in scikit-learn:

Supervised learning algorithms

  • LinearRegression - This function is used to perform linear regression, which is a supervised learning algorithm that can be used to predict a continuous value.
  • LogisticRegression - This function is used to perform logistic regression, which is a supervised learning algorithm that can be used to predict a categorical value.
  • DecisionTreeClassifier - This function is used to perform decision tree classification, which is a supervised learning algorithm that can be used to predict a categorical value.
  • RandomForestClassifier - This function is used to perform random forest classification, which is a supervised learning algorithm that can be used to predict a categorical value.
  • KNeighborsClassifier - This function is used to perform k-nearest neighbors classification, which is a supervised learning algorithm that can be used to predict a categorical value.
  • SupportVectorMachine - This function is used to perform support vector machine (SVM) classification, which is a supervised learning algorithm that can be used to predict a categorical value.

Unsupervised learning algorithms

  • KMeans - This function is used to perform k-means clustering, which is an unsupervised learning algorithm that can be used to group similar data points together.
  • DBSCAN - This function is used to perform density-based spatial clustering of applications with noise (DBSCAN), which is an unsupervised learning algorithm that can be used to group similar data points together.
  • GaussianMixture - This function is used to perform Gaussian mixture modeling, which is an unsupervised learning algorithm that can be used to model the distribution of data points.
  • PrincipalComponentAnalysis - This function is used to perform principal component analysis (PCA), which is a dimensionality reduction algorithm that can be used to reduce the number of features in a dataset.
  • T-SNE - This function is used to perform t-distributed stochastic neighbor embedding (t-SNE), which is a dimensionality reduction algorithm that can be used to visualize high-dimensional data.

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