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What is the difference between these source codes (XGBoost)

-2

$begingroup$

I want to know what is the difference between the two codes?

and which one do you advise me to use it?

Code 1:

# load data
# split data into (X_train, X_test, y_train, y_test)
from xgboost import XGBClassifier
model = XGBClassifier(learnin_rate=0.2, max_depth= 8,...)
eval_set = [(X_test, y_test)]
model.fit(X_train, y_train, eval_metric="auc", early_stopping_rounds=50, eval_set=eval_set, verbose=True)
y_pred = model.predict(X_test)

Code 2:

# load data
# split data into (X_train, X_test, y_train, y_test)
import xgboost as xgb
dtrain = xgb.DMatrix(X_train,y_train)
dtest = xgb.DMatrix(X_test,y_test)
eval_set = [(X_test, y_test)]
param = 'learnin_rate':0.2,'max_depth': 8, 'eval_metric':'auc', 'boost':'gbtree', 'objective': 'binary:logistic', ... 
num_round = 300
bst = xgb.train(param, dtrain, num_round)

machine-learning logistic-regression xgboost

share|improve this question

asked Mar 25 at 18:30

NirmineNirmine

236

$endgroup$

add a comment | 

-2

$begingroup$

I want to know what is the difference between the two codes?

and which one do you advise me to use it?

Code 1:

# load data
# split data into (X_train, X_test, y_train, y_test)
from xgboost import XGBClassifier
model = XGBClassifier(learnin_rate=0.2, max_depth= 8,...)
eval_set = [(X_test, y_test)]
model.fit(X_train, y_train, eval_metric="auc", early_stopping_rounds=50, eval_set=eval_set, verbose=True)
y_pred = model.predict(X_test)

Code 2:

# load data
# split data into (X_train, X_test, y_train, y_test)
import xgboost as xgb
dtrain = xgb.DMatrix(X_train,y_train)
dtest = xgb.DMatrix(X_test,y_test)
eval_set = [(X_test, y_test)]
param = 'learnin_rate':0.2,'max_depth': 8, 'eval_metric':'auc', 'boost':'gbtree', 'objective': 'binary:logistic', ... 
num_round = 300
bst = xgb.train(param, dtrain, num_round)

machine-learning logistic-regression xgboost

share|improve this question

asked Mar 25 at 18:30

NirmineNirmine

236

$endgroup$

add a comment | 

$begingroup$

I want to know what is the difference between the two codes?

and which one do you advise me to use it?

Code 1:

# load data
# split data into (X_train, X_test, y_train, y_test)
from xgboost import XGBClassifier
model = XGBClassifier(learnin_rate=0.2, max_depth= 8,...)
eval_set = [(X_test, y_test)]
model.fit(X_train, y_train, eval_metric="auc", early_stopping_rounds=50, eval_set=eval_set, verbose=True)
y_pred = model.predict(X_test)

Code 2:

# load data
# split data into (X_train, X_test, y_train, y_test)
import xgboost as xgb
dtrain = xgb.DMatrix(X_train,y_train)
dtest = xgb.DMatrix(X_test,y_test)
eval_set = [(X_test, y_test)]
param = 'learnin_rate':0.2,'max_depth': 8, 'eval_metric':'auc', 'boost':'gbtree', 'objective': 'binary:logistic', ... 
num_round = 300
bst = xgb.train(param, dtrain, num_round)

machine-learning logistic-regression xgboost

share|improve this question

asked Mar 25 at 18:30

NirmineNirmine

236

$endgroup$

# load data
# split data into (X_train, X_test, y_train, y_test)
from xgboost import XGBClassifier
model = XGBClassifier(learnin_rate=0.2, max_depth= 8,...)
eval_set = [(X_test, y_test)]
model.fit(X_train, y_train, eval_metric="auc", early_stopping_rounds=50, eval_set=eval_set, verbose=True)
y_pred = model.predict(X_test)

# load data
# split data into (X_train, X_test, y_train, y_test)
import xgboost as xgb
dtrain = xgb.DMatrix(X_train,y_train)
dtest = xgb.DMatrix(X_test,y_test)
eval_set = [(X_test, y_test)]
param = 'learnin_rate':0.2,'max_depth': 8, 'eval_metric':'auc', 'boost':'gbtree', 'objective': 'binary:logistic', ... 
num_round = 300
bst = xgb.train(param, dtrain, num_round)

share|improve this question

asked Mar 25 at 18:30

NirmineNirmine

236

share|improve this question

asked Mar 25 at 18:30

NirmineNirmine

236

asked Mar 25 at 18:30

NirmineNirmine

236

asked Mar 25 at 18:30

NirmineNirmine

236