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Feature selector is a tool for dimensionality reduction of machine learning datasets

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Not really an issue, but just wanting to be pointed in the right direction if such a thing is possible. During the “removal all” step, it uses a lightgbm to...

Hi, here is my code from feature_selector import FeatureSelector fs = FeatureSelector(data = df_ax_cla.loc[:,1:], labels = df_ax_cla.loc[:,0]) fs.identify_collinear(correlation_threshold=0.8): -------------------------------------------------------------------------- #here is error: AttributeError Traceback (most recent call last) in ---->...

Hi Will, Thanks for your wonderful tool named feature-selector.I have used it to select features from my project which is a supervised learning problem successfully.But ,I have no idea to...

I have a multi-class problem where some of class values only appears in a few register, so when i run this module i got this error. fs = FeatureSelector(data =...

Hello Will, Thanks for sharing your code. I tried my dataset using this code ``` from feature_selector import FeatureSelector import pandas as pd train = pd.read_csv('data/data_linkpower-Adela4.csv') train_labels = train['type_new'] print...

There are a situation,if my data have a feature with 100% missing values, or threshold like 98% missing values, call identify_collinear() will get more features with a correlation magnitude greater...

Add functionality to deal with datetimes in FeatureSelector. Currently the best option is to convert datetimes to seconds (for example seconds since the earliest time in the data).

When I try to run the code for collinear features: 222 # Add to dataframe `--> 223 record_collinear = record_collinear.append(temp_df, ignore_index = True)` 'DataFrame' object has no attribute 'append'

When the data volume and feature dimensions are extremely large, the default is all cpu usage, which is not very friendly for multiple users, and the program freezes

Thought it may be helpful/convenient to have a link to the license in the readme