2
$\begingroup$

How do I remove outliers from my data? Should I use RobustScaler? I am aware I can use DecisionTree but I want to use XGBoost...
Please can you help me, This is a bit urgent, I am not sure how to do it, I have researched and seen previous question but it doesnt work well and was not helpful.
Thank you

Cheers

$\endgroup$
1
$\begingroup$
  • First of all, you don't need to remove outlier because "Decision family algorithm" like XGBoost can handle it.

  • Secondly, you can use Tukey method (Tukey JW., 1977):

    def detect_outliers(df,n,features):
        outlier_indices = []
        # iterate over features(columns)
        for col in features:
            # 1st quartile (25%)
            Q1 = np.percentile(df[col], 25)
            # 3rd quartile (75%)
            Q3 = np.percentile(df[col],75)
            # Interquartile range (IQR)
            IQR = Q3 - Q1
            # outlier step
            outlier_step = 1.5 * IQR
            # Determine a list of indices of outliers for feature col
            outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step )].index
            # append the found outlier indices for col to the list of outlier indices 
            outlier_indices.extend(outlier_list_col)
            # select observations containing more than 2 outliers
            outlier_indices = Counter(outlier_indices)        
            multiple_outliers = list( k for k, v in outlier_indices.items() if v > n )
            return multiple_outliers 
    Outliers_to_drop = detect_outliers(data,2,["col1","col2"])
    data.loc[Outliers_to_drop] # Show the outliers rows
    # Drop outliers
    data= data.drop(Outliers_to_drop, axis = 0).reset_index(drop=True)
    

https://www.kaggle.com/yassineghouzam/titanic-top-4-with-ensemble-modeling

  • And Thirdly, I suggest you try discrete (binning) continuous variable instead of remove outlier for xgboost.
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.