Multiple logistische Regression

Fortgeschrittene Regression mit statsmodels in Python

Maarten Van den Broeck

Content Developer at DataCamp

Bank-Churn-Datensatz

has_churned	time_since_first_purchase	time_since_last_purchase
0	0.3993247	-0.5158691
1	-0.4297957	0.6780654
0	3.7383122	0.4082544
0	0.6032289	-0.6990435
...	...	...
response	Beziehungsdauer	Aktivitäts-Recency

¹ https://www.rdocumentation.org/packages/bayesQR/topics/Churn

logit()

from statsmodels.formula.api import logit

logit("response ~ explanatory", data=dataset).fit()

logit("response ~ explanatory1 + explanatory2", data=dataset).fit()

logit("response ~ explanatory1 * explanatory2", data=dataset).fit()

Die vier Ergebnisse

	vorhergesagt falsch	vorhergesagt wahr
tatsächlich falsch	korrekt	falsch positiv
tatsächlich wahr	falsch negativ	korrekt

conf_matrix = mdl_logit.pred_table()

print(conf_matrix)

[[102.  98.]
 [ 53. 147.]]

Ablauf der Vorhersage

from itertools import product

explanatory1 = some_values
explanatory2 = some_values

p = product(explanatory1, explanatory2)

explanatory_data = pd.DataFrame(p,
                                columns=["explanatory1",
                                         "explanatory2"])

prediction_data = explanatory_data.assign(
  mass_g = mdl_logit.predict(explanatory_data))

Visualisierung

prediction_data["most_likely_outcome"] = np.round(prediction_data["has_churned"])


sns.scatterplot(...
                data=churn,
                hue="has_churned",
                ...)

sns.scatterplot(...
                data=prediction_data,
                hue="most_likely_outcome",
                ...)

Lass uns üben!

Fortgeschrittene Regression mit statsmodels in Python