我已经完成了一个从文本中分类类别的机器学习算法。我已经完成了99%，但我现在不知道如何将我的预测结果合并回原始数据框，以查看我开始时的数据和预测结果的打印视图。
这是我的代码
#imports data from excel file and shows first 5 rows of datafile_name = r'C:\Users\aac1928\Documents\Machine Learning\Training Data\RFP Training Data.xlsx'sheet = 'Sheet1'import pandas as pdimport numpyimport xlsxwriterimport sklearndf = pd.read_excel(io=file_name,sheet_name=sheet)#extracts specifics rows from data data = df.iloc[: , [0,2]]print(data)#Gets data ready for modelnewdata = df.iloc[:,[1,2]]newdata = newdata.rename(columns={'Label':'label'})newdata = newdata.rename(columns={'RFP Question':'question'})print(newdata)# how to define X and yfor use with COUNTVECTORIZERX = newdata.questiony = newdata.labelprint(X.shape)print(y.shape)# split X and y into training and testing setsX_train = Xy_train = yX_test = newdata.question[:50]y_test = newdata.label[:50]print(X_train.shape)print(X_test.shape)print(y_train.shape)print(y_test.shape)# import and instantiate CountVectorizer (with the default parameters)from sklearn.feature_extraction.text import CountVectorizervect = CountVectorizer()# equivalently: combine fit and transform into a single stepX_train_dtm = vect.fit_transform(X_train)# transform testing data (using fitted vocabulary) into a document-term matrixX_test_dtm = vect.transform(X_test)X_test_dtm# import and instantiate a logistic regression modelfrom sklearn.linear_model import LogisticRegressionlogreg = LogisticRegression()# train the model using X_train_dtm%time logreg.fit(X_train_dtm, y_train)# make class predictions for X_test_dtmy_pred_class = logreg.predict(X_test_dtm)y_pred_class# calculate predicted probabilities for X_test_dtm (well calibrated)y_pred_prob = logreg.predict_proba(X_test_dtm)[:, 1]y_pred_prob# calculate accuracymetrics.accuracy_score(y_test, y_pred_class)这是我添加的新数据，用于进行预测，长度与数组相同
# split X and y into training and testing setsX_train = Xy_train = yX_testnew = dfpred.questiony_testnew = dfpred.labelprint(X_train.shape)print(X_testnew.shape)print(y_train.shape)print(y_testnew.shape)(447,)(168,)(447,)(168,)
# transform new testing data (using fitted vocabulary) into a document-term matrixX_test_dtm_new = vect.transform(X_testnew)X_test_dtm_new<168×1382 sparse matrix of type ” with 2240 stored elements in Compressed Sparse Row format>
# make class predictions for new X_test_dtmy_pred_class_new = nb.predict(X_test_dtm_new)y_pred_class_newarray([ 3, 3, 19, 18, 5, 10, 10, 5, 19, 3, 3, 3, 5, 3, 3, 3, 3, 9, 19, 5, 5, 10, 9, 5, 18, 19, 9, 9, 19, 19, 18, 18, 18, 4, 18, 3, 9, 18, 19, 19, 18, 19, 5, 19, 19, 3, 3, 18, 18, 5, 18, 3, 4, 5, 6, 4, 5, 19, 19, 5, 5, 19, 19, 4, 5, 18, 5, 5, 19, 5, 18, 5, 19, 18, 19, 5, 7, 5, 9, 9, 9, 9, 10, 9, 9, 5, 5, 5, 5, 3, 18, 4, 9, 5, 3, 6, 9, 18, 7, 5, 9, 5, 5, 19, 5, 5, 19, 5, 6, 5, 5, 6, 9, 21, 10, 9, 18, 9, 9, 3, 18, 5, 6, 18, 6, 3, 6, 5, 18, 6, 5, 18, 5, 6, 7, 7, 5, 7, 19, 18, 6, 5, 5, 5, 5, 5, 19, 16, 5, 19, 5, 5, 5, 5, 19, 5, 7, 19, 6, 7, 3, 18, 18, 18, 6, 19, 19, 7], dtype=int64)
# calculate predicted probabilities for X_test_dtm (well calibrated)y_pred_prob_new = logreg.predict_proba(X_test_dtm_new)[:, 1]y_pred_prob_newdf['prediction'] = pd.Series(y_pred_class_new)dfout = pd.merge(dfpred,df['prediction'].dropna() .to_frame(),how = 'left',left_index = True, right_index = True)print(dfout)
希望这对你有帮助，我尽量解释得尽可能清楚
回答：
我想因为你的预测结果只是一个数组，你最好直接使用：
df['predictions'] = y_pred_class