Code generated in the video can be downloaded from here:
github.com/bnsreenu/python_fo...
Let us start by understanding the Binary classification using keras . This is the normal way most of us approach the problem of binary classification
using sklearn (SVM). In this example, we will split our data set the normal way into train and test groups.
We will then learn to divide data using K Fold splits.
We will iterate through each split to train and evaluate our model.
We will finally use the cross_val_score() function to perform the evaluation.
It takes the dataset and cross-validation configuration and returns a list of
scores calculated for each fold.
KFOLD is a model validation technique.
Cross-validation between multiple folds allows us to evaluate the model performance.
KFold library in sklearn provides train/test indices to split data in train/test sets. Splits dataset into k consecutive folds (without shuffling by default).
Each fold is then used once as a validation while the k - 1 remaining folds
form the training set.
Split method witin KFold generates indices to split data into training and test set. The split will divide the data into n_samples/n_splits groups.
One group is used for testing and the remaining data used for training.
All combinations of n_splits-1 will be used for cross validation.
Wisconsin breast cancer example
Dataset link: www.kaggle.com/datasets/uciml...
28 фев 2023
