## How to use fit and transform for training and testing data with StandardScaler

As shown in the code below, I am using the StandardScaler.fit() function to fit (i.e., calculate the mean and variance from the features) the training dataset. Then, I call the ".transform()" function to scale the features. I found in the doc and here that I should use ".transform()" only to transform test dataset. In my case, I am trying to implement the anomaly detection model such that all training dataset is from one targeted user while all test dataset is collected from multiple other anomaly users. I mean, we have "n" users and we train the model using one class dataset samples from the targeted user while we test the trained model on new anamoly samples selected randomly from all other "n-1" anomaly users.

Training dataset size: (4816, 158) => (No of samples, No of features) Test dataset size: (2380, 158) The issue is the model gives bad results when I use fit() then "transform()" for the training dataset and only "transform()" for the test dataset. However, the model gives good results only when I use "fit_transform()" with both train and test datasets instead of only "transform()" for the test dataset.

My question: Should I follow the documentation of StandardScaler such that the test dataset MUST be transformed only using ".transform()" without fit() function? Or it depends on the dataset such that I can use the "fit_transform()" function for both training and testing datasets?

Is it possible if I use "fit_transform" for both training and testing dataset?

import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # After preparing and splitting the training and testing dataset, we got X_train # from only the targeted user X_test # from other "n-1" anomaly users # features selection using VarianceThreshold on training set sel = VarianceThreshold(threshold=(.8 * (1 - .8))) X_train= sel.fit_transform(X_train) #Normalization using StandardScaler scaler = StandardScaler().fit(X_train) normalized_X_train = scaler.transform(X_train) set_printoptions(precision=3) # features selection using VarianceThreshold on testing set X_test= sel.transform(X_test) #Normalization using StandardScaler normalized_X_test = scaler.transform(X_test) set_printoptions(precision=3)

Should I follow the documentation of StandardScaler such that the test dataset MUST be transformed only using ".transform()" without fit() function? Or it depends on the dataset such that I can use the "fit_transform()" function for both training and testing datasets?

The moment you are re-training your scaler for the testing set you will have a different dependincy of your input features. The original algorithm will be fitted based on the fitting of your training sacling. And if you re-train it this will be overwritten, and you are faking your input of the test data for the algorithm.

So the answer is **MUST** only be transformed.

**StandardScaler before and after splitting data,** In the interest of preventing information about the distribution of the test set leaking into your model, you should go for option #2 and fit the scaler on your training� As shown in the code below, I am using the StandardScaler.fit () function to fit (i.e., calculate the mean and variance from the features) the training dataset. Then, I call the ".transform ()" function to scale the features. I found in the doc and here that I should use ".transform ()" only to transform test dataset.

The way you do it above is correct. You should, in principle, never use `fit`

on test data, only on the train data. The fact that you get "better" results using `fit_transform`

on the test data is not indicative of any real performance gains. In other words, by using `fit`

on the test data, you lose the ability to say something meaningful about the predictive power of your model on unseen data.

The main lesson here is that any gains in test performance are meaningless once the methodological constraints (i.e. train-test separation) are violated. You may obtain higher scores using `fit_transform`

, but these don't mean anything anymore.

**sklearn.preprocessing.StandardScaler — scikit-learn 0.23.1 ,** where u is the mean of the training samples or zero if with_mean=False , and s is and standard deviation are then stored to be used on later data using transform . matrix which in common use cases is likely to be too large to fit in memory. To put it simply, you can use the fit_transform () method on the training set, as you’ll need to both fit and transform the data, and you can use the fit () method on the training dataset to get the value, and later transform () test data with it. Let me know if you have any comments or are not able to understand it.

when you want to transform a data you should declare that. like:

data["afs"]=data["afs"].transform()

**How to Use StandardScaler and MinMaxScaler Transforms in Python,** Sonar Dataset; MinMaxScaler Transform; StandardScaler Transform; Common Questions Fit the scaler using available training data. from the training set and are applied to all data sets (e.g., the test set or new samples). The data used to compute the mean and standard deviation used for later scaling along the features axis. y. Ignored. fit_transform (self, X, y=None, **fit_params) [source] ¶ Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters

**How to Save and Reuse Data Preparation Objects in Scikit-Learn,** Typically, the model fit on the training dataset is saved for later use. To make the idea of saving the object and data transform object to file We will use a test dataset from the scikit-learn dataset, specifically a binary� Here are the examples of the python api sklearn.preprocessing.StandardScaler.fit_transform taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

**fit, transform and fit_transform,** But transform() will use the mean of the train and apply it to test, would it not scaler = StandardScaler() scaler.fit(X_train) # get the 2 parameters from data (**μ � You can also use the method below will preprocess your data separately but similar parameter used for training data set. norm = preprocessing.Normalizer().fit(xtrain) then. x_train_norm = norm.transform(xtrain) x_test_norm = norm.transform(Xtest)

**Fit vs. Transform in SciKit libraries for Machine Learning,** For this, you'll use the fit() method on your training dataset to only the training dataset to get the value, and later transform() test data with it. Then, we’d use these parameters to transform our test data and any future data later on Let me give a hands-on example why this is important! 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”):