[英]How to predict on new data row using trained XGB classification model?
I trained a model with and got a decent auc.我训练了一个模型并获得了不错的 auc。 Now, I want to predict on completely new data but I am not sure how to.现在,我想预测全新的数据,但我不知道该怎么做。 Can someone help?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))
Now, I have a brand new data I want to score with this model.现在,我有一个全新的数据,我想用这个模型评分。 How would I do this?我该怎么做? Something predict.proba()?有什么predict.proba()?
Just fitting new data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)
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