Breaking News: Grepper is joining You.com. Read the official announcement!

python xgboost

Add Answer

orci answered on May 20, 2020 Popularity 10/10 Helpfulness 9/10

answer python xgboost

related xgboost python

related xgboost algorithm in python

related python XGBoost

python xgboost

Comment

#pip install xgboost
from xgboost import XGBClassifier,XGBRegressor
#Use classifier or regressor according to your problem
model = XGBClassifier()
model.fit(X_train, y_train)

Popularity 10/10 Helpfulness 9/10 Language python

Source: Grepper

Tags: python xgboost

Link to this answer
Share Copy Link

Contributed on Aug 03 2022

orci

0 Answers Avg Quality 2/10

Closely Related Answers

xgboost python

Comment

import xgboost as xgb
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test= train_test_split(X, y, test_size=0.2, random_state=123)
# Simple fit-predict
xg_cl = xgb.XGBClassifier(objective='binary:logistic', n_estimators=10, seed=123) # Classification
xg_reg = xgb.XGBRegressor(objective='reg:linear', n_estimators=10, seed=123) # Regression
model.fit(X_train, y_train)
preds = model.predict(X_test)

# Cross validation (Method 1 : Using the xgboost API, it has cv, train, predict which is unlike fit-predict in sklearn)
dmatrix = xgb.DMatrix(data=X_train, label=y_train)
params_clf={"objective":"binary:logistic","max_depth":4}        # Classification parameters
params_reg={"objective":"binary:logistic","booster":"gblinear"}  # Regression parameters with specified base learners
# Regularization parameters: "alpha" for l1, "lambda" for l2, "gamma" for penalty weight for splitting on a node according to tree complexity
cv_results = xgb.cv(dtrain=dmatrix, params=params_clf_reg, nfold=4, num_boost_round=10, 
        metrics="error", as_pandas=True, stratified=True, early_stopping_rounds=10, verbose_eval=1)
# accuracy_cv = 1 - cv_results['test-error-mean'].iloc[-1]
# Train the final model with the best number of boosting rounds
best_num_boost_round = len(cv_results)
final_model = xgb.train(params = params_clf_reg, dtrain = dmatrix, num_boost_round=best_num_boost_round)
# Make predictions on the testing dataset
dtest = xgb.DMatrix(X_test) # ,y_test
y_pred_prob = final_model.predict(dtest)
y_pred_binary = np.round(y_pred_prob)  # Convert probabilities to binary predictions
accuracy_final = accuracy_score(y_test, y_pred_binary)

# Cross validation (Method 2 : Using scikit-learn)
from sklearn.model_selection import cross_val_score, StratifiedKFold
xgb_model = xgb.XGBClassifier(objective="binary:logistic", random_state=42)
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
cv_scores = cross_val_score(xgb_model, X_train, y_train, cv=cv, scoring='accuracy')
from sklearn.model_selection import cross_val_predict
y_pred_cv = cross_val_predict(xgb_model, X_test, y=None, cv=cv)
accuracy_final = accuracy_score(y_test, y_pred_cv)

# GridSearch / RandomizedSearch (HYPERPARAMETER TUNING)
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
param_grid = {'learning_rate': np.arange(0.05,1.05,.05), 'n_estimators': [200], 'subsample': np.arange(0.05,1.05,.05)}
gbm = xgb.XGBRegressor()
tuning_models = Grid_RandomizedSearchCV(estimator=gbm, param_distributions=param_grid, n_iter=25, 
        scoring='neg_mean_squared_error', cv=4, verbose=1)
tuning_models.fit(X, y)
tuning_models.best_params_ # See the parameters that give the best results
# Visualize tree
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files/Graphviz/bin' ### MAKE SURE TO INSTALL GRAPHVIZ AND ADD THE INSTALLATION PATH
xgb.plot_tree(xg_model, num_trees=0) # , rankdir="LR" for aligning tree sideways from left to right

Popularity 6/10 Helpfulness 6/10 Language python

Source: Grepper

Tags: python

Link to this answer
Share Copy Link

Contributed on Jan 23 2024

Innocent Iguana

0 Answers Avg Quality 2/10

xgboost algorithm in python

Comment

xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
                max_depth = 5, alpha = 10, n_estimators = 10)

Popularity 10/10 Helpfulness 4/10 Language python

Source: www.datacamp.com

Tags: algorithm algo

Link to this answer
Share Copy Link

Contributed on May 23 2020

Wide-eyed Whale

0 Answers Avg Quality 2/10

xgboost algorithm in python

Comment

params = {"objective":"reg:linear",'colsample_bytree': 0.3,'learning_rate': 0.1,
                'max_depth': 5, 'alpha': 10}

cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,
                    num_boost_round=50,early_stopping_rounds=10,metrics="rmse", as_pandas=True, seed=123)

Popularity 10/10 Helpfulness 1/10 Language python

Source: www.datacamp.com

Tags: algorithm algo

Link to this answer
Share Copy Link

Contributed on May 23 2020

Wide-eyed Whale

0 Answers Avg Quality 2/10

python XGBoost

Comment

-2

# Create a placeholder to store the stock data
stock_data_dictionary = {}
for stock_name in stock_list:
# Get the data
df = data.get_data_yahoo(stock_name, start_date, end_date)
# Calculate the daily percent change
df['daily_pct_change'] = df['Adj Close'].pct_change()
# create the predictors
predictor_list = []
for r in range(10, 60, 5):
df['pct_change_'+str(r)] = df.daily_pct_change.rolling(r).sum()
df['std_'+str(r)] = df.daily_pct_change.rolling(r).std()
predictor_list.append('pct_change_'+str(r))
predictor_list.append('std_'+str(r))
# Target Variable
df['return_next_day'] = df.daily_pct_change.shift(-1)
df['actual_signal'] = np.where(df.return_next_day > 0, 1, -1)
df = df.dropna()
# Add the data to dictionary
stock_data_dictionary.update({stock_name: df})

Popularity 10/10 Helpfulness 1/10 Language python

Source: blog.quantinsti.com

Tags: python py

Link to this answer
Share Copy Link

Contributed on May 20 2020

Drab Donkey

0 Answers Avg Quality 2/10

python xgboost

Contents

More Related Answers

python xgboost

Closely Related Answers

xgboost python

xgboost algorithm in python

xgboost algorithm in python

python XGBoost

Grepper

Documentation

Social

Legal

Contact

Oops, You will need to install Grepper and log-in to perform this action.