如何使用经过训练的 XGB 分类模型预测新数据行?
How to predict on new data row using trained XGB classification model?
我训练了一个模型并获得了不错的 auc。现在,我想预测全新的数据,但我不确定该怎么做。有人可以帮忙吗?
# fit model no training data
model = XGBClassifier()
model.fit(X_train, y_train)
# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
#evaluate predictions train vs test data
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
现在,我有一个全新的数据要用这个模型评分。我该怎么做?东西 predict.proba()?
刚刚拟合新数据
NEW_DTA = pd.read_csv(data)
New_y_test = NEW_DTA.iloc[:,-1]
New_x_test = NEW_DTA.drop(colums='Target')
New_pred = model.predict(New_x_test)
我训练了一个模型并获得了不错的 auc。现在,我想预测全新的数据,但我不确定该怎么做。有人可以帮忙吗?
# fit model no training data
model = XGBClassifier()
model.fit(X_train, y_train)
# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
#evaluate predictions train vs test data
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
现在,我有一个全新的数据要用这个模型评分。我该怎么做?东西 predict.proba()?
刚刚拟合新数据
NEW_DTA = pd.read_csv(data)
New_y_test = NEW_DTA.iloc[:,-1]
New_x_test = NEW_DTA.drop(colums='Target')
New_pred = model.predict(New_x_test)