尝试使用我的决策树模型进行预测时，最后一行代码出现了上述错误。

     X=BTC_cleanData[-1:]---> print(regressor.predict(X))ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 145 is different from 146)

据我所知，我已经成功训练并测试了模型，但在尝试输出预测时出现了问题。我认为在定义要预测的目标时，某些操作导致矩阵中增加了一列，因此引发了matmul错误。我该如何编写一个能够正常工作的预测函数呢？

这是完整的代码，我省略了特征选择部分，因为它很长：

import pandas as pdimport numpy as npimport talibimport matplotlib.pyplot as plt%matplotlib inlineimport investpyfrom investpy import data from sklearn.tree import DecisionTreeRegressorfrom sklearn.model_selection import train_test_split#Import open, high, low, close, volume and Return data from csv using investpyBTC = data = investpy.get_crypto_historical_data(crypto='bitcoin', from_date='01/01/2014', to_date='06/08/2020')#Convert Data from Int to FloatBTC.Volume = BTC.Volume.astype(float)BTC.High = BTC.High.astype(float)BTC.Low = BTC.Low.astype(float)BTC.Close = BTC.Close.astype(float)#Drop Unnecessary Columnsdel BTC['Currency']#Select Indicators as FeaturesBTC['AD'] = talib.AD(BTC['High'].values, BTC['Low'].values, BTC['Close'].values, BTC['Volume'].values)...(there is a long list here)#Create forward looking columns using shiftBTC['NextDayPrice'] = BTC['Close'].shift(-1)#Copy dataframe and clean data BTC_cleanData = BTC.copy()BTC_cleanData.dropna(inplace=True)BTC_cleanData.to_csv('C:/Users/Admin/Desktop/BTCdata.csv')#Split Data into Training and Testing Set#separate the features and targets into separate datasets.#split the data into training and testing sets using a 70/30 split #Using splicing, separate the features from the target into individual data sets.  X_all = BTC_cleanData.iloc[:, BTC_cleanData.columns != 'NextDayPrice']  # feature values for all daysy_all = BTC_cleanData['NextDayPrice']  # corresponding targets/labelsprint (X_all.head())  # print the first 5 rows#Split the data into training and testing sets using the given feature as the targetfrom sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=0.30, random_state=42)from sklearn.linear_model import LinearRegression#Create a decision tree regressor and fit it to the training setregressor = LinearRegression()regressor.fit(X_train,y_train)print ("Training set: {} samples".format(X_train.shape[0]))print ("Test set: {} samples".format(X_test.shape[0]))#Evaluate Model (out of sample Accuracy and Mean Squared Error)from sklearn.model_selection import cross_validatefrom sklearn.model_selection import cross_val_scorescores = cross_val_score(regressor, X_test, y_test, cv=10)print ("accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2))    from sklearn.metrics import mean_squared_errormse = mean_squared_error(y_test, regressor.predict(X_test))print("MSE: %.4f" % mse)#Evaluate Model (In sample Accuracy and Mean Squared Error)trainscores = cross_val_score(regressor, X_train, y_train, cv=10)print ("accuracy: %0.2f (+/- %0.2f)" % (trainscores.mean(), trainscores.std() / 2))    mse = mean_squared_error(y_train, regressor.predict(X_train))print("MSE: %.4f" % mse)print(regressor.predict(X_train))#Predict Next Day PriceX=BTC_cleanData[-1:]print(regressor.predict(X))

回答：

您已经使用X_train数据训练了模型。要预测未见数据，您只需print(regressor.predict(X_test))。

之前您的代码是这样的：

X=BTC_cleanData[-1:] # 这比X_train和X_test多了一列print(regressor.predict(X))

但是，BTC_cleanData[-1:]比X_train和X_test多了一列。然而，模型是使用不包含这额外列的X_train训练的，这导致了错误。

清理后的工作代码：

import pandas as pdimport numpy as npimport talibimport matplotlib.pyplot as plt%matplotlib inlineimport investpyfrom investpy.crypto import get_crypto_historical_datafrom sklearn.tree import DecisionTreeRegressorfrom sklearn.metrics import mean_squared_errorfrom sklearn.model_selection import cross_validatefrom sklearn.model_selection import cross_val_scorefrom sklearn.linear_model import LinearRegressionfrom sklearn.model_selection import train_test_split#Import open, high, low, close, volume and Return data from csv using investpyBTC = get_crypto_historical_data(crypto='bitcoin', from_date='01/01/2014', to_date='06/08/2020')#Convert Data from Int to FloatBTC.Volume = BTC.Volume.astype(float)BTC.High = BTC.High.astype(float)BTC.Low = BTC.Low.astype(float)BTC.Close = BTC.Close.astype(float)#Drop Unnecessary Columnsdel BTC['Currency']#Select Indicators as FeaturesBTC['AD'] = talib.AD(BTC['High'].values, BTC['Low'].values, BTC['Close'].values, BTC['Volume'].values)#Create forward looking columns using shiftBTC['NextDayPrice'] = BTC['Close'].shift(-1)#Copy dataframe and clean data BTC_cleanData = BTC.copy()BTC_cleanData.dropna(inplace=True)#BTC_cleanData.to_csv('C:/Users/Admin/Desktop/BTCdata.csv')#Split Data into Training and Testing Set#separate the features and targets into separate datasets.#split the data into training and testing sets using a 70/30 split #Using splicing, separate the features from the target into individual data sets.  X_all = BTC_cleanData.iloc[:, BTC_cleanData.columns != 'NextDayPrice']  # feature values for all daysy_all = BTC_cleanData['NextDayPrice']  # corresponding targets/labelsprint (X_all.head())  # print the first 5 rows#Split the data into training and testing sets using the given feature as the targetX_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=0.30, random_state=42)#Create a decision tree regressor and fit it to the training setregressor = LinearRegression()regressor.fit(X_train,y_train)print ("Training set: {} samples".format(X_train.shape[0]))print ("Test set: {} samples".format(X_test.shape[0]))#Evaluate Model (out of sample Accuracy and Mean Squared Error)scores = cross_val_score(regressor, X_test, y_test, cv=10)print ("accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2))    mse = mean_squared_error(y_test, regressor.predict(X_test))print("MSE: %.4f" % mse)#Evaluate Model (In sample Accuracy and Mean Squared Error)trainscores = cross_val_score(regressor, X_train, y_train, cv=10)print ("accuracy: %0.2f (+/- %0.2f)" % (trainscores.mean(), trainscores.std() / 2))    mse = mean_squared_error(y_train, regressor.predict(X_train))print("MSE: %.4f" % mse)print(regressor.predict(X_train))#Predict Next Day Priceprint(regressor.predict(X_test))