Today we will discuss on StandardScaler, MinMaxScaler and RobustScaler techniques.

**StandardScaler** follows **Standard Normal Distribution (SND)**. Therefore, it makes mean=0 and scales the data to unit variance.

**MinMaxScaler** scales all the data features in the range [0,1] or else in the range [-1,1] if there are negative values in the dataset. This scaling compresses all the inliers in the narrow range [0,0.005].

In the presence of outliers, StandardScaler does not guarantee balanced feature scales, due to the influence of the outliers while computing the empirical mean and standard deviation. This leads to the shrinkage in the range of the feature values.

By using **RobustScaler()**, we can remove the outliers and then use either StandardScaler or MinMaxScaler for preprocessing the dataset.

Let us now look at how RobustScaler works.

class

sklearn.preprocessing.RobustScaler(

with_centering=True,

with_scaling=True,

quantile_range=(25.0, 75.0),

copy=True,

)

It scales features using statistics that are robust to outliers. This method removes the median and scales the data in the range between 1st quartile and 3rd quartile. i.e., in between 25th quantile and 75th quantile range. This range is also called an Interquartile range.

The median and the interquartile range are then stored so that it could be used upon future data using the transform method.

If outliers are present in the dataset, then the median and the interquartile range provide better results and outperform the sample mean and variance.

RobustScaler uses the interquartile range so that it is robust to outliers. Therefore its formula is as follows:

Below is the code that gives us the comparison between StandardScaler, MinMaxScaler and RobustScaler.

import pandas as pd

import numpy as np

from sklearn import preprocessing

import matplotlib

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

matplotlib.style.use('fivethirtyeight')

x = pd.DataFrame({# Distribution with lower outliers

'x1': np.concatenate([np.random.normal(20, 2, 1000), np.random.normal(1, 2, 25)]),# Distribution with higher outliers

'x2': np.concatenate([np.random.normal(30, 2, 1000), np.random.normal(50, 2, 25)]),

})

np.random.normal

scaler = preprocessing.RobustScaler()

robust_df = scaler.fit_transform(x)

robust_df = pd.DataFrame(robust_df, columns=['x1', 'x2'])

scaler=preprocessing.StandardScaler()

standard_df=scaler.fit_transform(x)

standard_df=pd.DataFrame(standard_df,columns=['x1','x2'])

scaler = preprocessing.MinMaxScaler()

minmax_df = scaler.fit_transform(x)

minmax_df = pd.DataFrame(minmax_df, columns=['x1', 'x2'])

fig, (ax1, ax2, ax3,ax4) = plt.subplots(ncols=4, figsize=(20, 5))

ax1.set_title('Before Scaling')

sns.kdeplot(x['x1'], ax=ax1,color='r')

sns.kdeplot(x['x2'], ax=ax1,color='b')

ax2.set_title('After Robust Scaling')

sns.kdeplot(robust_scaled_df['x1'], ax=ax2,color='red')

sns.kdeplot(robust_scaled_df['x2'], ax=ax2,color='blue')

ax3.set_title('After Standard Scaling')

sns.kdeplot(standard_df['x1'], ax=ax3,color='black')

sns.kdeplot(standard_df['x2'], ax=ax3,color='g')

ax4.set_title('After Min-Max Scaling')

sns.kdeplot(minmax_df['x1'], ax=ax4,color='black')

sns.kdeplot(minmax_df['x2'], ax=ax4,color='g')

plt.show()

fig.savefig('Graph.png')

The output obtained is as shown below:-

Below is the explanation for different parameters and attributes inside RobustScaler.

__Parameters:__

•** with_centering: boolean**

It is True by default. If the value is True, the data is centred before scaling. When it is applied on sparse matrices, the transform will raise an exception because centring them requires building a dense matrix, which generally is too large to fit in the memory.

• **with_scaling: boolean**

It is also set to True by default. It scales the data to the interquartile range.

• **quantile_range: tuple(q_min, q_max), 0.0 < q_min < q_max < 100.0**

Quantile range is used to calculate scale.

By default, it is set as below.

Default: (25.0, 75.0)= (1st quantile, 3rd quantile)=IQR.

• **copy: boolean**

It is an optional parameter. By default, it is True. If the input is already a NumPy array or a scipy.sparse CSC matrix and if axis=1, avoid a copy by setting this parameter to False and instead perform inplace row normalization.

__Attributes:__

• **center_: array of floats**

The median value for each feature in the training set.

• **scale_: array of floats**

Hope you understood the concept. Keep supporting for more contents.