Mean Encoding - Machine Learning Last Updated : 03 Jun, 2020 Comments Improve Suggest changes Like Article Like Report During Feature Engineering the task of converting categorical features into numerical is called Encoding. There are various ways to handle categorical features like OneHotEncoding and LabelEncoding, FrequencyEncoding or replacing by categorical features by their count. In similar way we can uses MeanEncoding. Created a DataFrame having two features named subjects and Target and we can see that here one of the features (SubjectName) is Categorical, so we have converted it into the numerical feature by applying Mean Encoding. Code: Python3 # importing libraries import pandas as pd # creating dataset data={'SubjectName':['s1','s2','s3','s1','s4','s3','s2','s1','s2','s4','s1'], 'Target':[1,0,1,1,1,0,0,1,1,1,0]} df = pd.DataFrame(data) print(df) Output: SubjectName Target 0 s1 1 1 s2 0 2 s3 1 3 s1 1 4 s4 1 5 s3 0 6 s2 0 7 s1 1 8 s2 1 9 s4 1 10 s1 0 Code : Counting every datapoints in SubjectName Python3 df.groupby(['SubjectName'])['Target'].count() Output: subjectName s1 4 s2 3 s3 2 s4 2 Name: Target, dtype: int64 Code: groupby data with SubjectName with their mean according to their positive target value Python3 df.groupby(['SubjectName'])['Target'].mean() Output: subjectName s1 0.750000 s2 0.333333 s3 0.500000 s4 1.000000 Name: Target, dtype: float64 The output shows the mean mapped with data point in SubjectName with their positive target value (1-positive and 0-Negative). Code : Finally assigning the mean value and map with df['SubjectName'] Python3 Mean_encoded_subject = df.groupby(['SubjectName'])['Target'].mean().to_dict() df['SubjectName'] = df['SubjectName'].map(Mean_encoded_subject) print(df) Output : Mean Encoded Data SubjectName Target 0 0.750000 1 1 0.333333 0 2 0.500000 1 3 0.750000 1 4 1.000000 1 5 0.500000 0 6 0.333333 0 7 0.750000 1 8 0.333333 1 9 1.000000 1 10 0.750000 0 Pros of MeanEncoding: Capture information within the label, therefore rendering more predictive features Creates a monotonic relationship between the variable and the target Cons of MeanEncodig: It may cause over-fitting in the model. Comment More infoAdvertise with us Next Article Mean Encoding - Machine Learning V Vikash_Kumar_Chaurasia Follow Improve Article Tags : Machine Learning python Practice Tags : Machine Learningpython Similar Reads Maths for Machine Learning Mathematics is the foundation of machine learning. Math concepts plays a crucial role in understanding how models learn from data and optimizing their performance. Before diving into machine learning algorithms, it's important to familiarize yourself with foundational topics, like Statistics, Probab 5 min read What is Machine Learning? Machine learning is a branch of artificial intelligence that enables algorithms to uncover hidden patterns within datasets. It allows them to predict new, similar data without explicit programming for each task. Machine learning finds applications in diverse fields such as image and speech recogniti 9 min read Statistics For Machine Learning Machine Learning Statistics: In the field of machine learning (ML), statistics plays a pivotal role in extracting meaningful insights from data to make informed decisions. Statistics provides the foundation upon which various ML algorithms are built, enabling the analysis, interpretation, and predic 7 min read How does Machine Learning Works? Machine Learning is a subset of Artificial Intelligence that uses datasets to gain insights from it and predict future values. It uses a systematic approach to achieve its goal going through various steps such as data collection, preprocessing, modeling, training, tuning, evaluation, visualization, 7 min read Linear Regression in Machine learning Linear regression is a type of supervised machine-learning algorithm that learns from the labelled datasets and maps the data points with most optimized linear functions which can be used for prediction on new datasets. It assumes that there is a linear relationship between the input and output, mea 15+ min read Like