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Machine Learning FAQ

Also, optimization algorithms such as gradient descent work best if our features are centered at mean zero with a standard deviation of one — i.e., the data has the properties of a standard normal distribution.

(How do we estimate mean and standard deviation if we have only 1 data point?) That’s an intuitive case to show why we need to keep and use the training data parameters for scaling the test set.

Let’s imagine we have a simple training set consisting of 3 samples with 1 feature column (let’s call the feature column “length in cm”): Given the data above, we compute the following parameters: If we use these parameters to standardize the same dataset, we get the following values: Now, let’s say our model has learned the following hypotheses: It classifies samples with a standardized length value <

However, if we standardize these by re-computing the standard deviation and and mean from the new data, we would get similar values as before (i.e., properties of a standard normal distribtion) in the training set and our classifier would (probably incorrectly) assign the “class 2” label to the samples 4 and 5.

Get Your Data Ready For Machine Learning in R with Pre-Processing

Preparing data is required to get the best results from machine learning algorithms.

In this post you will discover how to transform your data in order to best expose its structure to machine learning algorithms in R using the caret package.

You will work through 8 popular and powerful data transforms with recipes that you can study or copy and paste int your current or next machine learning project.

Finally, your raw data may not be in the best format to best expose the underlying structure and relationships to the predicted variables.

It is important to prepare your data in such a way that it gives various different machine learning algorithms the best chance on your problem.

You can use rules of thumb such as: These are heuristics, but not hard and fast laws of machine learning, because sometimes you can get better results if you ignore them.

In the next section you will discover how you can apply data transforms in order to prepare your data in R using the caret package.

You can learn more about the data transforms provided by the caret package by reading the help for the preProcess function by typing ?preProcess and by reading the Caret Pre-Processing page.

The data transforms presented are more likely to be useful for algorithms such as regression algorithms, instance-based methods (like kNN and LVQ), support vector machines and neural networks.

In this section you discovered 8 data preprocessing methods that you can use on your data in R via the caret package: You can practice with the recipes presented in this section or apply them on your current or next machine learning project.

When scale the data, why the train dataset use 'fit' and 'transform', but the test dataset only use 'transform'?

have tried to explain the intuition behind this logic below: We decide to scale both the features in the training dataset before applying linear regression and fitting the linear regression function.

When we scale the features of the training dataset, all 'x1' features get adjusted according to the mean and the standard deviations of the different samples w.r.t to their 'x1' feature values.

Now what happens when we fit the linear regression function is that it learns the parameters (i.e, learns to predict the response values) based on the scaled features of our training dataset.

That means that it is learning to predict based on those particular means and standard deviations of 'x1' and 'x2' of the different samples in the training dataset.

Which in turn depend on the *value of the features of the training data (which have been scaled).And because of the scaling the training data's features depend on the *training data's mean and std.

Rescaling Data for Machine Learning in Python with Scikit-Learn

The data preparation process can involve three steps: data selection, data preprocessing and data transformation.

Your preprocessed data may contain attributes with a mixtures of scales for various quantities such as dollars, kilograms and sales volume.

It is useful to scale the input attributes for a model that relies on the magnitude of values, such as distance measures used in k-nearest neighbors and in the preparation of coefficients in regression.

good tip is to create rescaled copies of your dataset and race them against each other using your test harness and a handful of algorithms you want to spot check.

In this post you discovered where data rescaling fits into the process of applied machine learning and two methods: Normalization and Standardization that you can use to rescale your data in Python using the scikit-learn library.

Normalize Data

Rescales numeric data to constrain dataset values to a standard range Category: Data Transformation / Scale and Reduce This article describes how to use the Normalize Data module in Azure Machine Learning Studio, to transform a dataset through normalization.

Normalization avoids these problems by creating new values that maintain the general distribution and ratios in the source data, while keeping values within a scale applied across all numeric columns used in the model.

The Normalize Data module generates two outputs: For examples of how normalization is used in machine learning, see the Azure AI Gallery: This module supports only the standard normalization methods listed in the How to section, and does not support matrix normalization or other complex transforms.

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