Where to find feature normalization in machine learning?
If you’re new to data science/machine learning, you probably wondered a lot about the nature and effect of the buzzword ‘feature normalization’. If you’ve read any Kaggle kernels, it is very likely that you found feature normalization in the data preprocessing section.
When to build a ML model for deployment?
When building an ML-model for deployment purposes, you must always have your end-users in mind. A model with thousands of features to attain an accuracy 90+% on evaluation might not be good enough for deployment for several reasons:
When to use a base line ML model?
Chapters 3 and 4 are covered in Part 2 of this series. A base-line model in this context refers to an ML-model having the minimum possible number of features but with good evaluation measures. When building an ML-model for deployment purposes, you must always have your end-users in mind.
How does standardization improve numerical stability in machine learning?
Standardization improves the numerical stability of your model If we have a simple one-dimensional data X and use MSE as the loss function, the gradient update using gradient descend is: X is in the gradient descent formula, which means the value of X determines the update rate.
When to use normalization in a neural network?
Normalization is useful when your data has varying scales and the algorithm you are using does not make assumptions about the distribution of your data, such as k-nearest neighbors and artificial neural networks.
How is scaling to unit length used in machine learning?
Scaling to unit length shrinks/stretches a vector (a row of data can be viewed as a D -dimensional vector) to a unit sphere. When used on the entire dataset, the transformed data can be visualized as a bunch of vectors with different directions on the D -dimensional unit sphere.
Is it necessary to rescale data in machine learning?
Although not required, you can often get a boost in performance by carefully choosing methods to rescale your data. In this post you will discover how you can rescale your data so that all of the data has the same scale.