doc: add changelog for tracking issues

CHANGELOG.md

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+# Changes
+
+## 1.0.6
+
+ - Return self in mrmr, fixing error when in scikit-learn pipeline
+
+## 1.0.5
+
+ - Patching classes where old unused argument was causing an error
+
+## 1.0.2
+
+ - Distribute a toy dataset for regression by modifying the Boston dataset adding noise and made up columns
+
+## 1.0.1
+
+ - Fix pkg data distribution
+
+## 1.0.0
+
+ - Parallelization of functions applied on pandas data frame
+ - Faster and more modular association measures 
+ - Removing dependencies (e.g. dython)
+ - Better static and interactive visualization
+ - Sklearn selectors rather than a big class
+ - Discretization of continuous and categorical predictors
+ - Minimal redundancy maximal relevance feature selection added (a subset of all relevant predictors), based on Uber's MRmr flavor
+ - architecture closer to the scikit-learn one
+
+## 0.3.8
+
+ - Fix bug when compute shap importance for classifier in GrootCV
+
+## 0.3.7
+
+ - Add defensive check if no categorical found in the subsampling of the dataset
+ - Re-run the notebooks with the new version
+## 0.3.6
+
+ - Fix clustering when plotting only strongly correlated predictors
+ - Remove palettable dependencies for plotting 
+ - Add default colormap but implement the user defined option
+## 0.3.5
+
+- Enable clustering before plotting the correlation/association matrix, optional
+- Decrease fontsize for the lables of the correlation matrix
+
+## 0.3.4
+
+ - Update requirements 
+
+## 0.3.3
+
+ - Upgrade documentation
+
+## 0.3.2
+
+ - Fix typo for distributing the dataset and pinned the dependencies
+## 0.3.1 
+
+ - Update the syntax for computing associations using the latest version of dython
+
+## 0.3.0
+
+ - Fix the Boruta_py feature counts, now adds up to n_features
+ - Fix the boxplot colours, when only rejected and accepted (no tentative) the background color was the tentative color
+ - Numpy docstring style
+ - Implement the new lightGBM callbacks. The new lgbm version (>3.3.0) implements the early stopping using a callback rather than an argument
+ - Fix a bug for computing the shap importance when the estimator is lightGBM and the task is classification
+ - Add ranking and absolute ranking attributes for all the classes
+ - Fix future pandas TypeError when computing numerical values on a dataframe containing non-numerical columns
+ - Add housing data to the distribution
+ - Add "extreme" sampling methods
+ - Re-run the NBs
+ - reindex to keep the original columns order
+
+## 0.2.3
+
+ - Update syntax to stick to the new argument names in Dython
+
+## 0.2.2
+
+ - Check if no feature selected, warn rather than throw error
+
+## 0.2.1
+
+ - Fix a bug when removing collinear columns
+
+## 0.2.0
+
+ - Prefilters now support the filtering of continuous and nominal (categorical) collinear variables
+
+## 0.1.6
+
+ - improve the plot_y_vs_X function
+ - remove gc.collect()
+
+## 0.1.5
+
+ - fix readme (typos)
+ - move utilities in utils sub-package
+ - make unit tests lighter
+
+## 0.1.4
+
+ - fix bug when using catboost, clone estimator (avoid error and be sure to use a non-fitted estimator)
+
+## 0.1.3
+
+ - change the defaut for categorical encoding in pre-filters (pd.cat to integers as default)
+ - fix the unit tests with new defaults and names
+
+## 0.1.2
+
+ - change arguments name in pre-filters
+
+## 0.1.1
+
+ - remove old attribute names in unit-tests
+
+## 0.1.0
+
+ - Fix lightGBM warnings
+ - Typo in repr
+ - Provide load_data utility
+ - Enhance jupyter NB examples
+ - highlighting synthetic random predictors
+ - Benchmark using sklearn permutation importance
+ - Harmonization of the attributes and parameters
+ - Fix categoricals handling
+
+## 0.0.4
+
+ - setting optimal number of features (according to "Elements of statistical learning") when using lightGBM random forest boosting.
+ - Providing random forest, lightgbm implementation, estimators
+
+## 0.0.3
+
+ - Adding examples and expanding documentation
+
+## 0.0.2
+
+ - fix bug: relative import removed