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 classification model like logistic regression 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_model import 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.
- 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)
This time we don’t receive any error because the response values for the model are categorical.
The following tutorials explain how to fix other common errors in Python: