Day #92 - Mean Encoding

Mean Coding

• Add new variables based on certain features
• Label encoding is done usually
• Mean encoding is done as variable count / distinct unique variables
• The proportion of label encoding also is included in this step
• Min encoding with label encoding
• Label encoding - No logical order
• Mean encoding - Classes are separable
• We can reach better loss with sorted trees
• Trees need huge number of splits 
• Model tries to treat all categories differently

Constructing Mean Encoding

• Goods - Number of ones in a group
• Bads - Number of zeros

Likelihood = Goods/(Goods + Bads) = mean(target)
Weight of Evidence = In(Goods/Bads)*100
Count = Goods = sum(target)
Diff = Goods-Bads

	means = x_tr.groupby(col).target.mean()
	train_new[col+'_mean_target'] = train_new[col].map(means)
	val_new[col+'_mean_target'] = val_new[col].map(means)
	means
	#Fit XGBoost model on this new data
	dtrain = xgb.DMatrix(train_new,label=y_tr)
	dvalid = xgb.Dmatrix(val_new,label=y_val)
	evalist = [(dtrain,'train'),(dvalid,'eval')]
	evals_result3 = {}
	model = xgb.train(xgb_par,dtrain,3000,evals=evalist,verbose_eval=30,evals_result=evals_result3,early_stopping_rounds=50)

view raw mean_encoding.py hosted with ❤ by GitHub

Happy Learning!!!