How is Linear Regression model from sklearn predicting non-linearly in the following code?

Question

Since linear regression algorithms find the best fit line for the training data, so the forecast for the new data will always line on that best fit line. Then how is Linear Regression model from sklearn predicting data non-linearly as shown in this image. !(https://pythonprogramming.net/static/images/machine-learning/linear-regression-prediction.png)

import Quandl, math
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, svm
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime

style.use('ggplot')

df = Quandl.get("WIKI/GOOGL")
df = df[['Adj. Open',  'Adj. High',  'Adj. Low',  'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0

df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(value=-99999, inplace=True)
forecast_out = int(math.ceil(0.01 * len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)

X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out]

df.dropna(inplace=True)

y = np.array(df['label'])

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
clf = LinearRegression(n_jobs=-1)
clf.fit(X_train, y_train)
confidence = clf.score(X_test, y_test)

forecast_set = clf.predict(X_lately)
df['Forecast'] = np.nan

last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day

for i in forecast_set:
    next_date = datetime.datetime.fromtimestamp(next_unix)
    next_unix += 86400
    df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]

df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

it is linear in respect to 'HL_PCT', 'PCT_change', 'Adj. Volume', not to time — Stef

bjschoenfeld bjschoenfeld · Accepted Answer · 2019-09-07T21:07:04

The model produced by linear regression is linear in all of the predictive features, i.e. X. Your model appears to be trained with the features 'HL_PCT', 'PCT_change', 'Adj. Volume'. However, the plot contains only one feature on the X-axis (as all 2D plots do), Date, which is not even one of your predictive features. Even if Date were one of your predictive features in X, the projection from multiple dimensions down to 1 could make the model look non-linear.

How is Linear Regression model from sklearn predicting non-linearly in the following code?

1 Answers