Installing Auto-Sklearn Properly using python 3.5+

auto-sklearn installation requires python 3.5 or higher. In addition, it also has dependencies on the packages mentioned here:

Better approach is to have a python 3.5+ environment. And then using pip install auto-sklearn.

  • Check which version/path are you using – which pythonwhich pip
  • Install python 3.5 or higher, if you don’t have it already: steps to follow
  • Once you have the correct version of python installed, set up a virtual environment of the python3.5. Follow the code to setup a virtual environment:

python3 -m pip install --user virtualenv

source env/bin/activate

  • Finally call pip install auto-sklearn


  • In case you are using anaconda, following command will start your virtual env:conda update conda #Update your current version of condaconda create --name py35 python=3.5 #creat e a virtual env for python 3.5source activate py35 #activate the environment

Post your query here, again in case you are not sure of the steps.


How to begin a Supervised Machine Learning Problem in Kaggle!

Identifying the Problem Type:

It is important in Machine learning to understand the problem type first. If it is continuous output – [1,23,4,5,6, 5.5, 6.7,..], use Linear Regression. If it is a categorical output – [0,1,0,0,1…] or [‘High’, ‘low’, ‘Medium’, …] etc., go for Logistic Regression. Since your target labels are either 0 or 1, this is a problem to be worked with Logistic Regression or other Classification algorithms (SVM, Decision Tree, Random Forest).

Data Cleaning/Exploration:

You must convert your data to numeric format or standardized format for regression.

Starter Code:

In case you are looking for a starter code for your problem, you can find that from Kaggle kernels. Here are a few links: