Feature Selection in Machine Learning • Information about the target class is intrinsic in the variables • More info does not mean more discrimination power • Dimensionality and Performance - Required #samples grows exponentially with #variables - Classifier’s performance degrades for a large number of features.The following are 2 code examples of.
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mutual_info_classif can only take numeric data. You need to do label encoding of the categorical features and then run the same code. x1=x.apply (LabelEncoder ().fit_transform) Then run the exact same code you were running. mutual_info_classif (x1, y, discrete_features= [1, 2, 3]) Share Improve this answer edited Mar 30, 2020 at 1:34.
I get the concept of Mutual Information and feature selection, I just don't understand how it is implemented in Python. What I do is that I provide the mutual_info_score method with two arrays based on the NLP site example, but it outputs different results. The other interesting fact is that anyhow you play around and change numbers on those.
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from sklearn.feature_selection import mutual_info_classif as MIC mi_score = MIC (X,y) print (mi_score) You shall see the mi_score array like this: [0.37032947 0.09670886 0.40294198 0.36009957 0.08427789 0.21318114 0.37337734 0.43985571 0.06456878 0.00276314 0.24866738 0.00189163 0.27600984 0.33955538 0.01503326 0.07603828 0.11825812 0.12879402.
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Assessment of SHAP as feature selection mechanism; A library with Python implementation of the methodol- ... methods use the different incremental estimation of Mutual Information , ,  or classiﬁers’ weights  to classify features based on importance and perform feature selection.For example, the concept of weights is used.Feature Selection in python is.
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Encode the categorical variables prior to feature selection. encoder = ce.LeaveOneOutEncoder (return_df= True ) X = encoder.fit_transform (X_cat, y) Feature Selection Select the Top N Start with 63 features after dropping target leakage features. X.shape ( 10108, 63 ) Select the top 20 features.
For example, we can define the SelectKBest class to use the f_classif () function and select all features, then transform the train and test sets. 1 2 3 4 5 6 7 8 9 ... # configure to select all features fs = SelectKBest(score_func=f_classif, k='all') # learn relationship from training data fs.fit(X_train, y_train) # transform train input data
Feature selection is one of the crucial parts of entire process begining with data collection and ending with modelling. If you are developing in python, scikit learn offers you enormous built-in ...
Mutual information is one of many quantities that measures how much one random variables tells us about another. It is a dimensionless quantity with (generally) units of bits, and can be thought of as the reduction in uncertainty about one random variable given knowledge of another.High mutual information indicates a large reduction in uncertainty; low mutual . Zhang