cas-pml/SL/aufgaben/template/4_WS/WS 08 Vorlage.ipynb

WS 08 Regression mit Standardisieren und Logarithmieren

• untersuchen Sie den Einfluss des Standardisierens der Features auf folgende Ergebnisse der Linearen Regression:
  • Modellkoeffizienten
  • Predictions
  • Score
• untersuchen Sie den Einfluss des Logarithmierens des Targets auf die Performance der Linearen Regression

In [3]:

## prepare env, read and prepare data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()

codepath = '../2_code' ## for import of user defined module
datapath = '../3_data'
#codepath = '.././2_code' ## for import of user defined module
#datapath = '../../3_data'

from sys import path; path.insert(1, codepath)
from os import chdir; chdir(datapath)

from bfh_cas_pml import prep_data
X_train, X_test, y_train, y_test = prep_data('melb_data_prep.csv', target='Price', seed=1234)

In [4]:

## baseline
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(model.intercept_)
print(model.coef_[:6])
print(y_pred[:6])
print(r2_score(y_test, y_pred))

-105513873.23403685
[ 245383.60581414 -141356.39759052  -40383.66643969  161336.03949841
   40391.14829949   83303.27089591]
[1331246.16325189 2557493.2373921   871684.82823291 1495633.275723
 1549557.61151302  634348.67092323]
0.5601419746121152

In [5]:

## scaled features
## tbd

In [6]:

## log target
## tbd

Fazit

• tbd