Models - Logistic Regression, (Statsmodel), .ipynb

Kernel: Python 3

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import json
%matplotlib inline

# set max printout options for pandas:
pd.options.display.max_columns = 50
pd.options.display.max_colwidth = 300

df = pd.read_table('../data/evergreen_sites.tsv')

df['is_news'] = df['is_news'].str.replace('?','0').astype(int)

pd.crosstab(df['is_news'], df['label'], margins=True)

Test the hypothesis with...

Logistic Regression using statsmodels.

The sm.logit function from statsmodels.formula.api will perform a logistic regression using a formula string.

import statsmodels.formula.api as sm

from scipy import stats
stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)


import statsmodels.formula.api as smf
result = smf.logit('label ~ is_news', data=df)
result = result.fit()
result.summary()

Optimization terminated successfully.
         Current function value: 0.692751
         Iterations 3

Logistic Regression

# Fit a logistic regression model and store the class predictions.

from sklearn.linear_model import LogisticRegression

logreg = LogisticRegression() #create object

#feature_cols = []
X = df[['is_news']] #create X (if you are passing a single column or array, you need to double [[]] so that it reads as a df)
y = df['label']  #create y

logreg.fit(X, y) #fit
pred = logreg.predict(X) #predict

logreg.score(X, y) #this returns the accuracy

0.5133198106828939