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Fitting a logistic model

The university studying the relationship between hours of study and outcomes on a given test has provided you with a dataset containing the number of hours the students studied and whether they failed or passed the test, and asked you to fit a model to predict future performance.

The data is provided in the variables hours_of_study and outcomes. Use this data to fit a LogisticRegression model. numpy has been imported as np for your convenience.

Diese Übung ist Teil des Kurses

Foundations of Probability in Python

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Anleitung zur Übung

  • Import LogisticRegression from sklearn.linear_model.
  • Create the model using LogisticRegression(C=1e9).
  • Pass the data to the model.fit() method.
  • Create variables for each parameter, assign the values from the model, and print the parameters beta1 and beta0.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Import LogisticRegression
from sklearn.linear_model import ____

# sklearn logistic model
model = ____(C=1e9)
model.____(____, ____)

# Get parameters
beta1 = model.coef_[0][0]
beta0 = model.intercept_[0]

# Print parameters
print(____, ____)
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