Splitting Datasets With scikit-learn and train_test_split()
Darren Jones
29:17
Description
One of the key aspects of supervised machine learning is model evaluation and validation. When you evaluate the predictive performance of your model, it’s essential that the process be unbiased. Using train_test_split() from the data science library scikit-learn, you can split your dataset into subsets that minimize the potential for bias in your evaluation and validation process.
In this course, you’ll learn:
In addition, you’ll get information on related tools from sklearn.model_selection.
What You'll Learn?
- Why you need to split your dataset in supervised machine learning
- Which subsets of the dataset you need for an unbiased evaluation of your model
- How to use
train_test_split()to split your data - How to combine
train_test_split()with prediction methods
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Darren Jones
Instructor's CoursesDarren is an avid Pythonista and creates video tutorials for Real Python. Having followed a varied career as a nuclear industry instrumentation engineer, a musician, recording engineer and teacher, he’s always been interested in computers, spending the last 20 years producing music with them and teaching music technology. He’s written books on the subject, and spends the remainder of his time improving his Python skills, with a view to bringing them into the studio and classroom for better understanding of both music and programming.
Real Python
View courses Real Python- language english
- Training sessions 11
- duration 29:17
- Release Date 2023/01/05
