title: Given feature/column names do not match the ones for the data given during fit. Error
body: <p>I wrote following code and it gives me this error :</p>
  <blockquote>
  <p>&quot;Given feature/column names do not match the ones for the data given
  during fit.&quot;</p>
  </blockquote>
  <p>Train and predict data has the same features.</p>
  <pre><code>df_train = data_preprocessing(df_train)
  
  #Split X and Y
  X_train = df_train.drop(target_columns,axis=1)
  y_train = df_train[target_columns]
  
  #Create a boolean mask for categorical columns
  categorical_columns = X_train.columns[X_train.dtypes == 'O'].tolist()
  
  # Create a boolean mask for numerical columns
  numerical_columns = X_train.columns[X_train.dtypes != 'O'].tolist()
  
  # Scaling &amp; Encoding objects
  numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])
  
  categorical_transformer = OneHotEncoder(handle_unknown='ignore')
  
  col_transformers = ColumnTransformer(
                          # name, transformer itself, columns to apply
                          transformers=[(&quot;scaler_onestep&quot;, numeric_transformer, numerical_columns),
                          (&quot;ohe_onestep&quot;, categorical_transformer, categorical_columns)])
  
  #Manual PROCESSING
  model = MultiOutputClassifier(
          xgb.XGBClassifier(objective=&quot;binary:logistic&quot;,
                          colsample_bytree = 0.5
                          ))
  
  #Define a pipeline
  pipeline = Pipeline([(&quot;preprocessing&quot;, col_transformers), (&quot;XGB&quot;, model)])
  
  pipeline.fit(X_train, y_train)
  
  #Data Preprocessing
  predicted = data_preprocessing(predicted)
  X_predicted = predicted.drop(target_columns,axis=1)
  
  predictions=pipeline.predict(X_predicted)
  </code></pre>
  <p>I got error on prediction process. How can i fix this problem? I couldn't find any solution.</p>