Normalization is an important step in machine learning that helps make your model work better. Simply put, normalization means adjusting numerical input features to be in the same range. This is important because machine learning algorithms usually perform better when the features are on a similar scale.
Avoiding Bias: Some algorithms, like those that use gradient descent (for example, linear regression and neural networks), are affected by the scale of the input features. If we don't normalize, features with larger numbers can take over the learning process. For example, think about a dataset that has height in centimeters and weight in kilograms. In this case, height might be more important than weight when training the model.
Speeding Up Learning: Normalization can help the learning process go faster. When the features are on similar scales, the algorithm can find the best solution more directly.
Min-Max Scaling: This method changes the feature so that its values range from 0 to 1. It uses the following formula:
Z-score Normalization (Standardization): This technique adjusts the data so that it has an average of 0 and a standard deviation of 1:
Let’s say you’re creating a model to predict housing prices. If you have features like square footage (1,500 sq. ft.) and age (5 years), the difference in their scales can confuse the algorithm. Normalizing these features makes sure the model treats both measurements equally, which helps it predict prices more accurately and efficiently.
In conclusion, normalization is key to getting your data ready for machine learning. It levels the playing field for different features, which helps your models perform at their best.
Normalization is an important step in machine learning that helps make your model work better. Simply put, normalization means adjusting numerical input features to be in the same range. This is important because machine learning algorithms usually perform better when the features are on a similar scale.
Avoiding Bias: Some algorithms, like those that use gradient descent (for example, linear regression and neural networks), are affected by the scale of the input features. If we don't normalize, features with larger numbers can take over the learning process. For example, think about a dataset that has height in centimeters and weight in kilograms. In this case, height might be more important than weight when training the model.
Speeding Up Learning: Normalization can help the learning process go faster. When the features are on similar scales, the algorithm can find the best solution more directly.
Min-Max Scaling: This method changes the feature so that its values range from 0 to 1. It uses the following formula:
Z-score Normalization (Standardization): This technique adjusts the data so that it has an average of 0 and a standard deviation of 1:
Let’s say you’re creating a model to predict housing prices. If you have features like square footage (1,500 sq. ft.) and age (5 years), the difference in their scales can confuse the algorithm. Normalizing these features makes sure the model treats both measurements equally, which helps it predict prices more accurately and efficiently.
In conclusion, normalization is key to getting your data ready for machine learning. It levels the playing field for different features, which helps your models perform at their best.