One common error you may encounter in Python is:

ValueError: Unknown label type: 'continuous'

This error usually occurs when you attempt to use sklearn to fit a like and the values that you use for the response variable are continuous instead of categorical.

The following example shows how to use this syntax in practice.

How to Reproduce the Error

Suppose we attempt to use the following code to fit a logistic regression model:

import numpy as np
from sklearn.linear_modelimport LogisticRegression

#define values for predictor and response variables
x = np.array([[2, 2, 3], [3, 4, 3], [5, 6, 6], [7, 5, 5]])
y = np.array([0, 1.02, 1.02, 0])

#attempt to fit logistic regression model
classifier = LogisticRegression()
classifier.fit(x, y)

ValueError: Unknown label type: 'continuous'

We receive an error because currently the values for our response variable are continuous.

Recall that a requires the values of the response variable to be such as:

• 0 or 1
• “Yes” or “No”
• “Pass” or “Fail”

Currently our response variable contains continuous values such as 0 and 1.02.

How to Fix the Error

The way to resolve this error is to simply convert the continuous values of the response variable to categorical values using the LabelEncoder() function from sklearn:

from sklearn import preprocessing
from sklearn import utils

#convert y values to categorical values
lab = preprocessing.LabelEncoder()
y_transformed = lab.fit_transform(y)

#view transformed values
print(y_transformed)

[0 1 1 0]

Each of the original values is now encoded as a 0 or 1.

We can now fit the logistic regression model:

#fit logistic regression model
classifier = LogisticRegression()
classifier.fit(x, y_transformed)

Additional Resources

The following tutorials explain how to fix other common errors in Python: