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Shravan Kumar Koninti replied to Vishal Kapur's discussion Missing Value in DATA - Approaches

"Missing value in dataset can be handled by 4 types: 1) You can delete those records from dataset 2) You can do Mean/ Mode/ Median Imputation. 3) If you know modelling, then you can use prediction model 4) KNN Imputation - The missing values of…"

Nov 4

Shravan Kumar Koninti's discussion was featured

Error while running the XGboost for Multiclass problem

Hi Everyone,I want to solve a classification problem which has 10 classes to predict.my train and validation datasets are prepared properly.here is my code. Please let me know why this error is coming.X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)# Success print ("Training and testing split was successful.")import xgboost as xgbdtrain = xgb.DMatrix(X_train, label=y_train) dtest = xgb.DMatrix(X_test, label=y_test)param = { 'max_depth': 3, # the maximum…See More

Oct 10

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Shravan Kumar Koninti posted a discussion

Error while running the XGboost for Multiclass problem

Hi Everyone,I want to solve a classification problem which has 10 classes to predict.my train and validation datasets are prepared properly.here is my code. Please let me know why this error is coming.X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)# Success print ("Training and testing split was successful.")import xgboost as xgbdtrain = xgb.DMatrix(X_train, label=y_train) dtest = xgb.DMatrix(X_test, label=y_test)param = { 'max_depth': 3, # the maximum…See More

Oct 10

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