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functions-definitions-and-parameters.md

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what is f1 score?

  • f1 score is the harmonic mean of precision and recall

what is precision?

  • precision is the number of true positives divided by the number of true positives plus the number of false positives

what is recall?

  • recall is the number of true positives divided by the number of true positives plus the number of false negatives

what is accuracy?

  • accuracy is the number of correct predictions divided by the total number of predictions

random forest classifier and parameters

  • random forest classifier is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting
  • parameters => n_estimators, criterion, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes, min_impurity_decrease, min_impurity_split, bootstrap, oob_score, n_jobs, random_state, verbose, warm_start, class_weight . n_estimators => the number of trees in the forest . criterion => the function to measure the quality of a split . max_depth => the maximum depth of the tree . min_samples_split => the minimum number of samples required to split an internal node

KNN classifier and parameters

  • KNN classifier is a non-parametric method used for classification and regression
  • parameters => n_neighbors, weights, algorithm, leaf_size, p, metric, metric_params, n_jobs
  • n_neighbors => number of neighbors to use by default for kneighbors queries

Decision Tree Classifier and parameters

  • Decision Tree Classifier is a class capable of performing multi-class classification on a dataset
  • parameters => criterion, splitter, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, random_state, max_leaf_nodes, min_impurity_decrease, min_impurity_split, class_weight, presort
  • criterion => the function to measure the quality of a split
  • splitter => the strategy used to choose the split at each node
  • max_depth => the maximum depth of the tree

Support Vector Machine and parameters

  • Support Vector Machine is a supervised machine learning model which is used for classification and regression problems
  • parameters => C, kernel, degree, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state
  • C => penalty parameter C of the error term
  • kernel => specifies the kernel type to be used in the algorithm

K-Means Clustering and parameters

  • K-Means Clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining
  • parameters => n_clusters, init, n_init, max_iter, tol, precompute_distances, verbose, random_state, copy_x, n_jobs, algorithm
  • n_clusters => the number of clusters to form as well as the number of centroids to generate
  • init => method for initialization
  • max_iter => maximum number of iterations of the k-means algorithm for a single run

different methods of feature selection

  • univariate feature selection
  • recursive feature elimination
  • incremental feature selection
  • decremental feature selection

what is correlation and what is np.corrcoef?

  • correlation is a statistical measure that expresses the extent to which two variables are linearly related
  • np.corrcoef => computes the correlation coefficient between two arrays
  • example => np.corrcoef([1, 0, 1], [0, 2, 1])