lamb = [0.0001, 0.1, 1, 10]
xg = XGBClassifier(n_estimators=20,
reg_lambda=0.0001,
colsample_bytree=0.1,
max_delta_step=0,
colsample_bylevel=0.1,
max_depth=5,
learning_rate=0.1,
silent=True,
reg_alpha=0.5,
subsample=0.9,
gamma=1,
min_child_weight=9)
estim = [30, 100, 200]
for i in lamb:
xg.reg_lambda = i
score1 = cross_val_score(xg, Xtrain, ytrain, cv=3)
print(i, np.mean(score1))
''' for j in delt :
xg.max_delta_step=j
score1=cross_val_score(xg,Xtrain,ytrain,cv=3)
print(i,j,np.mean(score1))'''
#xg=XGBClassifier(n_estimators=100,reg_lambda=0.01,colsample_bytree=0.1,max_delta_step=0,colsample_bylevel=0.1,max_depth=5,learning_rate=0.1,silent=True,reg_alpha=1,subsample=0.9,gamma=1,min_child_weight=9)
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain1 = scaler.transform(Xtrain)
Xtest1 = scaler.transform(Xtest)
test1 = scaler.transform(test)
pca = PCA(n_components=100)
