Here’s what I’ve learnt about Sklearn.resample | by Samson Afolabi | Towards Data Science

Working with imbalanced dataset can be a tough nut to crack for data scientist. One of the ways at which you deal with imbalanced datasets is by resampling with sklearn.resample i.e. upsampling the minority class or downsampling the majority class.
Sklearn.resample is Scikit learn’s function for upsampling/downsampling.
From sklearn documentation, the function sklearn.resample, resamples arrays or sparse matrices in a consistent way and the default strategy implements one step of the bootstrapping procedure. In simple terms, sklearn.resample doesn’t just generate extra data points to the datasets by magic, it basically creates a random resampling(with/without replacement) of your dataset. This equalization procedure prevents the Machine Learning model from inclining towards the majority class in the dataset.
Next, I show upsampling in an example. In the example below we create a dataframe with 3 columns: age, sex and store.
#import librariesimport pandas as pdfrom sklearn.utils import resample,shuffle#create a dataframedf = {'age':['a','b','c','a','b'],'sex':['e','f','g','f','e'],'store':[1,2,3,3,2]}df = pd.DataFrame(df)df.head()
We first, separate the minority class and then the upsample the minority class. The size of the minority class is upsampled to the size of the other classes.
#set the minority class to a seperate dataframedf_1 = df[df['store'] == 1]#set other classes to another dataframeother_df = df[df['store'] != 1]  #upsample the minority classdf_1_upsampled = resample(df_1,random_state=42,n_samples=2,replace=True)#concatenate the upsampled dataframedf_upsampled = pd.concat([df_1_upsampled,other_df])df_upsampled
How cool is that! 😉 😊 Now we have more data to train our model with. Also each of the 3 store categories (1,2 and 3) in the data can be observed in equal amounts when we train the model.
However, when you upsample or downsample, avoid these mistakes!
In a Machine Learning problem, make sure to upsample/downsample ONLY AFTER you split into train, test (and validate if you wish). If you do upsample your dataset before you split into train and test, there is a high possibility that your model is exposed to data leakage. See an Example below.
In a Machine Learning problem, make sure to upsample/downsample ONLY AFTER you split into train, test (and validate if you wish). If you do upsample your dataset before you split into train and test, there is a high possibility that your model is exposed to data leakage. See an Example below.
from sklearn.model_selection import train_test_splitX = df_upsampled.drop('store',axis=1)y = df_upsampled.storeX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1,shuffle=True)X_train.head()
X_test.head()
Notice the data leakage! We have exactly the same data point in X_train as well as X_test. Doing this might give us a wrong sense of what our Machine Learning model is really performing.
2. After your Machine Learning Model is built, it is advisable to test your metric on your NOT-UPSAMPLED train dataset. Testing your metric on the NOT-UPSAMPLED data set gives you a more realistic estimate of your model than testing it on the UPSAMPLED dataset. Personally, I always like to keep a version of the train dataset that wasn’t upsampled.
Conclusion:Upsampling/downsampling are very good approaches in handling unbalanced data. However it is important to understand how they work, so as to be able to use them correctly. Note also that the upsampling mechanism introduces bias into your system because of the additional information.
You can also read about the SMOTE operator of the imbean library. It works based on the KNearestNeighbours algorithm, synthetically generating data points that fall in the proximity of the already existing outnumbered group. Read more about it here.
I hope this was helpful for you. Looking forward to your comments here, meanwhile you can also follow me on twitter and Linkedin.