Logistic regression is a statistical way used to express an outcome in advance based on the observations before. It is used to define data and to express the connection between one dependent twofold variable, nominal, figure, and ratio-level independent changeable. Moreover, there are more than two classes of the response changeable; it’s considered as multinomial logistic regression. In addition, logistic regression was appropriated from statistics and is one of the most common twofold classification algorithms in machine studying and data science.
Below is an example when using the LogisticRegression and how to resolve this problem. Thus, we will learn about the “ValueError: Found array with dim 3. Estimator expected <= 2” when operating the LogisticRegression model.
How does it happen?
The error occurs when you try to add the code in the LogisticRegression in the notebook. That can not be updated yet in the notebook or dataset does not fit with the system. Let’s check the array that you are using, it is qualified with the system. Because the inappropriate package is the cause of the problem and it does not work.
lr = LogisticRegression()
lr.fit(train_dataset,train_labels)
The following text was displayed as follows: ValueError: Found array with dim 3. Estimator expected <= 2.
How can you fix “ValueError: Found array with dim 3. Estimator expected <= 2.” error in the the Logistic Regression?
Your package in the system is not appropriate like applying the display of 2d, instead the array in a 3d. If you change the fit array it will be well-functioning. Consequently, we are confident that you are able to correct this error by applying the lot into a 2d.
nsamples, nx, ny = train_dataset.shape
d2_train_dataset = train_dataset.reshape((nsamples,nx*ny))
Conclusion
Above all, we hope you like this article about solving the Logistic Regression “ValueError: Found array with dim 3. Estimator expected <= 2” Error. We make sure that with our explanation above, you will be able to fix most of your notebook by updating the suitable function. If you need further information for this problem, please feel free to give us a comment below. Please share our useful posts with others.