max_depth = range(1, 51)
train_score = []
test_score = []
X = vecs_train
y = df_train1.Sentiment
for k in max_depth:
dt = XGBClassifier(random_state=42, max_depth=k)
dt.fit(X, y)
train_score.append(dt.score(vecs_train, df_train1.Sentiment))
test_score.append(dt.score(vecs_dev, df_dev1.Sentiment))
plt.plot(max_depth, train_score, label="train")
plt.plot(max_depth, test_score, label="test")
plt.legend()
print(f"Test Score: {np.max(test_score)}")
print(f"n ke: {max_depth[np.argmax(test_score)]}")
ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_916/2795825213.py in <module>
8 for k in max_depth:
9 dt = XGBClassifier(random_state=42, max_depth=k)
---> 10 dt.fit(X, y)
11
12 train_score.append(dt.score(vecs_train, df_train1.Sentiment))
c:\users\roihan\miniconda3\lib\site-packages\xgboost\core.py in inner_f(*args, **kwargs)
573 for k, arg in zip(sig.parameters, args):
574 kwargs[k] = arg
--> 575 return f(**kwargs)
576
577 return inner_f
c:\users\roihan\miniconda3\lib\site-packages\xgboost\sklearn.py in fit(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)
1356 ):
1357 raise ValueError(
-> 1358 f"Invalid classes inferred from unique values of `y`. "
1359 f"Expected: {expected_classes}, got {self.classes_}"
1360 )
ValueError: Invalid classes inferred from unique values of `y`. Expected: [0 1 2], got [1 2 3]
i use fasttext to make word or vector weights, then i use that vector for DecisionTreeClassifier and it works, but when i use XGBClassifier i get an error message then after i try i don't get a solution for the error like in the picture
