ValueError: Unable to coerce to Series, length must be 1: given n

Question

I have been tried to use RF regression from scikit-learn , bu I face a problem with my standard (from docs and tutorials) model.. so there is code:

    import pandas as pd
    import numpy as np
    from sklearn.ensemble import RandomForestRegressor

    db = pd.read_excel('/home/artyom/myprojects//valuevo/field2019/report/segs_inventar_dataframe/excel_var/invcents.xlsx')

    age = df[['AGE_1', 'AGE_2', 'AGE_3', 'AGE_4', 'AGE_5']]

    hight = df [['HIGHT_','HIGHT_1', 'HIGHT_2', 'HIGHT_3', 'HIGHT_4', 'HIGHT_5']]

    diam = df[['DIAM_', 'DIAM_1', 'DIAM_2', 'DIAM_3', 'DIAM_4', 'DIAM_5']]

    za = df[['ZAPSYR_', 'ZAPSYR_1', 'ZAPSYR_2', 'ZAPSYR_3', 'ZAPSYR_4', 'ZAPSYR_5']]

    tova = df[['TOVARN_', 'TOVARN_1', 'TOVARN_2', 'TOVARN_3', 'TOVARN_4', 'TOVARN_5']]

    #df['average'] = df.mean(numeric_only=True, axis=1)


    df['meanage'] = age.mean(numeric_only=True, axis=1)
    df['meanhight'] = hight.mean(numeric_only=True, axis=1)
    df['mediandiam'] = diam.mean(numeric_only=True, axis=1)
    df['medianza'] = za.mean(numeric_only=True, axis=1)
    df['mediantova'] = tova.mean(numeric_only=True, axis=1)

    unite = df[['gapA_segA','gapP_segP', 'A_median', 'p_median', 'circ_media','fdi_median', 'pfd_median', 'p_a_median', 'gsci_media','meanhight']].dropna()

    from sklearn.model_selection import train_test_split as ttsplit

    df_copy = unite.copy()
    trainXset = df_copy[['gapA_segA','gapP_segP', 'A_median', 'p_median', 'circ_media','fdi_median', 'pfd_median', 'p_a_median', 'gsci_media']]

    trainYset = df_copy [['meanhight']]

    trainXset_train, trainXset_test, trainYset_train, trainYset_test = ttsplit(trainXset, trainYset, test_size=0.3) # 70% training and 30% test

    rf = RandomForestRegressor(n_estimators = 100, random_state = 40)
    rf.fit(trainXset_train, trainYset_train)
    predictions = rf.predict(trainXset_test)
    errors = abs(predictions - trainYset_test)
    mape = 100 * (errors / trainYset_test)
    accuracy = 100 - np.mean(mape)
    print('Accuracy:', round(accuracy, 2), '%.')

But output isn't look ok:

---> 24 errors = abs(predictions - trainYset_test)
     25 # Calculate mean absolute percentage error (MAPE)
     26 mape = 100 * (errors / trainYset_test)
..... somemore track
ValueError: Unable to coerce to Series, length must be 1: given 780

How I cant to fix it? 780 it is a .shape of trainYset_test. I don't ask for simple solve(do code for me), but for advice why mistake have thrown. I did all like in tutorials.

Solution

by seeing in error it is cleared that, the array has to have the shape of one ,

so use reshape to make it in correct shape,

predictions=predictions.reshape(780,1)