scikit-learn library can be used to code machine learning classifier and is the only Python library which has four-step modeling pattern.
Refer to the following link for more information about sckit-learn: http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf.
The coding process of implementing the scikit-learn model applies to various classifiers within sklearn, such as decision trees, k-nearest neighbors (KNN), and more. We will look at a few of these classifiers here, using our well logging data.
The first step in using Scikit to build a model is to create training and test datasets and apply scaling, using the following lines of Python code:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train...
