Challenge 1: Data Scaling

In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.

Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:

Uppgift

Swipe to start coding

In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.

Use the StandardScaler class to standardize the dataset, which means transforming it to have a mean of 0 and a standard deviation of 1.
Use the MinMaxScaler class to rescale the dataset. Ensure that after scaling, the feature values lie between -1 and 1.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 7. Kapitel 1

Challenge 1: Data Scaling

Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:

Uppgift

Swipe to start coding

In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.

Use the StandardScaler class to standardize the dataset, which means transforming it to have a mean of 0 and a standard deviation of 1.
Use the MinMaxScaler class to rescale the dataset. Ensure that after scaling, the feature values lie between -1 and 1.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 7. Kapitel 1

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan