Inverse Feature Scaling not working while predicting results-CodePudding

# Importing required libraries
import numpy as np
import pandas as pd

# Importing dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1: -1].values
y = dataset.iloc[:, -1].values

y = y.reshape(len(y), 1)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
scy = StandardScaler()
scX = StandardScaler()
X = scX.fit_transform(X)
y = scy.fit_transform(y)

# Training SVR model
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

# Predicting results from SCR model
# this line is generating error
scy.inverse_transform(regressor.predict(scX.transform([[6.5]])))

I am trying to execute this code to predict values from the model but after running it I am getting errors like this:

ValueError: Expected 2D array, got 1D array instead:
array=[-0.27861589].
Reshape your data either using an array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Complete Stack trace of error:

Even my instructor is using the same code but his one is working mine one not I am new to machine learning can anybody tell me what I am doing wrong here. Thanks for your help. This is the data for reference

CodePudding user response：

It is because of the shape of your predictions, the scy is expecting an output with (-1, 1) shape.
Change your last line to this:

scy.inverse_transform([regressor.predict(scX.transform([[6.5]]))])

You can also use this line to predict:

pred = regressor.predict(scX.transform([[6.5]]))
pred = pred.reshape(-1, 1)
scy.inverse_transform(pred)