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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
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Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
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Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
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Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
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Pickle and Joblib Introduction to Model Deployment
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Model Deployment in Android Model Deployment in Iphone

Introductory Note on Imputation Techniques

A
Alifia Ghantiwala Last Updated : 22 Mar, 2022
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Machine learning models are garbage in garbage-out boxes, and it is essential to address any missing data before feeding it to your model.

Missing data in your dataset could be due to multiple reasons like

1) The data was not available.

2) The information was not recorded due to a data entry error and so on.

It is essential to handle them appropriately instead of completely ignoring them, as they might represent crucial information and affect your model’s performance.

We will look into different imputation techniques from basic to advanced as part of this article. Let’s begin!

Make No Change

Tree-based models like Light GBM and XGBoost can work with NA values; you could try a baseline model with your missing data and check the performance metric.

Another approach would be to drop the rows with missing values; you can do this when the records with missing values are a small percentage of your entire dataset. Otherwise, it would not make any sense to drop so much information.

During the EDA stage, try to visualize the percentage of missing data and make an informed choice.

The plot of missing data is a part of the work I am currently doing in the WIDS competition on Kaggle.

ncounts = pd.DataFrame([train.isna().mean(), test.isna().mean()]).T
ncounts = ncounts.rename(columns={0: "train_missing", 1: "test_missing"})
ncounts.query("train_missing > 0").plot(
    kind="barh", figsize=(8, 5), title="% of Values Missing"
)
plt.show()


ncounts.query("train_missing > 0")


Image Source: Author


As you can see, for some features, almost 50% of records in train data and 80% of records in test data are missing. In such cases, you may want to remove the feature altogether as it may not be providing any meaningful contribution to the predicted target.


You can also visualize the number of missing features per record as below.


tt["n_missing"] = tt[nacols].isna().sum(axis=1)
 train["n_missing"] = train[nacols].isna().sum(axis=1)
 test["n_missing"] = test[nacols].isna().sum(axis=1)
 tt["n_missing"].value_counts().plot(
 kind="bar", title="Number of Missing Values per Sample"
 )


 


We can see that we have almost 30000 records with four missing features and some 824 records with six features missing, such an analysis would help you make an informed choice on the imputation technique you would want to use, or would you not want to use anything at all.


Imputation by a Constant Value or a Mean/Median/Mode


You can impute the missing data by replacing it with a constant value; in our case, we have some missing data for the column year built since the competition is running in the current year, i.e. 2022, a reasonable choice to replace missing values of the feature would be 2022. The same can be done as below in python.


 train['year_built'] =train['year_built'].replace(np.nan, 2022)
 test['year_built'] =test['year_built'].replace(np.nan, 2022)


Another approach would be to replace the missing values with the mean of the non-empty records in the feature. As mean is susceptible to outliers, you can also use mean or median as the replacement strategy.


Code examples as below


test['energy_star_rating']=test['energy_star_rating'].replace(np.nan,test['energy_star_rating'].mean())
 test['energy_star_rating']=test['energy_star_rating'].replace(np.nan,test['energy_star_rating'].median())


Another interesting strategy is imputing a feature with missing values based on another part.


For example, suppose you want to fill in the missing value for max wind speed based on the building class. In that case, the record belongs to; you would first do a group by on the feature building class and calculate the mean of max wind speed in all individual groups, following which impute the missing values in max wind speed based on the building class the record belongs to.


Simple Imputer


Sklearn provides a similar strategy as we discussed above to impute missing values by a constant or an average value.


It is always good to know alternate methods to perform the same task, which is why we look at the below code using Simple Imputer in action.


from sklearn.impute import SimpleImputer
 imptr = SimpleImputer(strategy="mean")
 tr_imp = imptr.fit_transform(train[FEATURES])
 test_imp = imptr.transform(test[FEATURES])


Based on your use case, you can replace the strategy by mean, mode, median, or constant.


Advanced Imputation Techniques


Under this topic, we will look at the below imputation techniques.


    

  • Iterative Imputer
    • 

  • KNN Imputer
    • 

  • LGBM Imputer
    • 



Iterative Imputer


Under the hood, its implementation involves imputing missing values by modelling each feature as a function of other elements round-robin fashion.


You can also understand this simply because the missing values are considered targets, and the remaining features are used to predict their values.


It uses the Bayesian Ridge algorithm internally.


Let’s see the same in action with the below python code.


it_imputer = IterativeImputer(max_iter=10)
train_iterimp = it_imputer.fit_transform(X[FEATURES])
test_iterimp = it_imputer.transform(X_test[FEATURES])
# Create train test imputed dataframe
X_df = pd.DataFrame(train_iterimp, columns=FEATURES)
X_test_df = pd.DataFrame(test_iterimp, columns=FEATURES)


KNN Imputer


The imputer works on the same principles as the K nearest neighbour unsupervised algorithm for clustering. It uses KNN for imputing missing values; two records are considered neighbours if the features that are not missing are close to each other.


Logically, it does make sense to impute values based on its nearest neighbour. You can give it a try and check your cross-validation score for an improvement or otherwise on your dataset.


Below is the code to get started with the KNN imputer


from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2)
imputer.fit_transform(X)


n_neighbors parameter specifies the number of neighbours to be considered for imputation.


LGBM Imputer


It uses LightGBM to impute missing values in features; you can refer to the entire implementation of the imputer by Hiroshi Yoshihara here.


!git clone https://github.com/analokmaus/kuma_utils.git
import sys
sys.path.append("kuma_utils/")
from kuma_utils.preprocessing.imputer import LGBMImputer
lgbm_imtr = LGBMImputer(n_iter=100, verbose=True)
train_lgbmimp = lgbm_imtr.fit_transform(train[FEATURES])
test_lgbmimp = lgbm_imtr.transform(test[FEATURES])
tt_lgbmimp = lgbm_imtr.fit_transform(tt[FEATURES])
tt_imp = pd.DataFrame(tt_lgbmimp, columns=FEATURES)
# Create LGBM Train/Test imputed dataframe
lgbm_imp_df = pd.DataFrame(tt_imp, columns=FEATURES)




Code reference:- here


Conclusion


We have discussed multiple techniques for imputing missing values as part of this article, and I hope you have learned something new from it. There is no one solution fits all mechanism for imputation; you may have to try different approaches and see which works best to your cross-validation score; a general guideline would be to start with a baseline model with mean imputation and build up from there.


If you have any questions or feedback suggestions, you can provide the same in the comments below. I write these articles to improve upon my understanding of applied machine learning. You can connect with me on Linkedin or read about me here. Hope you liked my article on imputation techniques, share it in the comments below.


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The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 

                                                

                            

                                

                                    
                                      
                                  A
                                  
                                
                                

                                

                                    
                                    Alifia Ghantiwala                                  
                                                            

                            

                            
I investigate data on a daily basis to find insights! I write so that I can understand more clearly. Have completed my graduation in Computer Engineering, and won my first public data science competition last month, March 2021, hosted on Kaggle by Google Developers.

                        

                        
                         
                    

                                       
                                        

                        
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