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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?
Reading Data Files into Python Different Variable Datatypes
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
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
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
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
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
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
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
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
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
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
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
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Machine Learning with Limited Data

Parth Shukla
Parth Shukla Last Updated : 21 Dec, 2022
5 min read

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

Introduction

In machine learning, the data’s amount and quality are necessary to model training and performance. The amount of data affects machine learning and deep learning algorithms a lot. Most of the algorithm’s behaviors change if the amount of data is increased or decreased. But in the case of limited data, it is necessary to effectively handle machine learning algorithms to get better results and accurate models. Deep learning algorithms are also data-hungry, requiring a large amount of data for better accuracy.

In this article, we will discuss the relationship between the amount and the quality of the data with machine learning and deep learning algorithms, the problem with limited data, and the accuracy of dealing with it. Knowledge about these key concepts will help one understand the algorithm vs. data scenario and will shape one so that one can deal with limited data efficiently.

The “Amount of Data vs. Performace” Graph

In machine learning, a query could be raised to your mind, how strictly is the data required to train a good machine learning or deep learning model? Well, there is no threshold levels or fixed answer to this, as every piece of information is different and has different features and patterns. Still, there are some threshold levels after which the performance of the machine learning or deep learning algorithms tends to be constant.

Most of the time, machine learning and deep learning models tend to perform well as the amount of data fed is increased, but after some point or some amount of data, the behavior of the models becomes constant, and it stops learning from data.

Limited Data

The above pictures show the performance of some famous machine learning and deep learning architectures with the amount of data fed to the algorithms. Here we can see that the traditional machine learning algorithms learn a lot from the data in a preliminary period, where the amount of data fed is increasing, but after some time, when a threshold level comes, the performance becomes constant. Now, if you provide more data to the algorithm, it will not learn anything, and the version will not increase or decrease.

In the case of deep learning algorithms, there are a total of three types of deep learning architectures in the diagram. The shallow ty[e of deep learning stricture is a minor deep learning architecture in terms of depth, meaning that there are few hidden layers and neurons in external deep learning architectures. In the case o deep neural networks, the number of hidden layers and neurons is very high and designed very profoundly.

From the diagram, we can see a total of three deep learning architectures, and all three perform differently when some amount of data is fed and increased. The shallow, deep neural networks tend to function like traditional machine learning algorithms, where the performance becomes constant after some threshold amount of data. At the same time, deep neural networks keep learning from the data when new data is fed.

From the diagram, we can conclude that,

” THE DEEP NEURAL NETWORKS ARE DATA HUNGRY “

What Problems Arise with Limited Data?

Several problems occur with limited data, and the model could perform better if trained with limited data. The common issues that arise with limited data are listed below:

1. Classification: 

In classification, if a low amount of data is fed, then the model will classify the observations wrongly, meaning that it will not give the accurate output class for given words.

2. Regression:

In a regression problem, if the model’s accuracy is low, then the model will predict very wrong, meaning that as it is a regression problem, it will be expecting the number. Still, limited data may show a horrifying amount far from the actual output.

3. Clustering:

The model can classify the different points in the wrong clusters in the clustering problems if trained with limited data.

4. Time Series:

In time series analysis, we forecast some data for the future. Still, a low-accurate time series model can give us inferior forecast results, and there may be a lot of errors related to time.

5. Object Detection:

If an object detection model is trained on limited data, it might not detect the object correctly, or it can classify the thing incorrectly.

How to Deal With Problems of Limited Data?

There needs to be an accurate or fixed method for dealing with the limited data. Every machine learning problem is different, and the way of solving the particular problem is other. But some standard techniques are helpful for many cases.

Limited Data

1. Data Augmentation

Data augmentation is the technique in which the existing data is used to generate new data. Here the further information generated will look like the old data, but some of the values and parameters would be different here.

This approach can increase the amount of data, and there is a high likelihood of improving the model’s performance.

Data augmentation is preferred in most deep-learning problems, where there is limited data with images.

2. Don’t Drop and Impute:

In some of the datasets, there is a high fraction of invalid data or empty. Due to that, some amount of data s dropped not to make the process complex, but by doing this, the amount of data is decreased, and several problems can occur.

To deal with it, we can apply the data imputation technique to attribute the data. Although emptying the data is not a simple and accurate method, some advanced attributes like KNNImputer and IterativeImputer can be used for accurate and efficient data imputation.

3. Custom Approach:

If there is a case of limited data, one could search for the data on the internet and find similar data. Once this type of data is obtained, it can be used to generate more data or be merged with the existing data.

The domain knowledge can help lit in this case. The domain expert can advise and guide through this problem very efficiently and accurately.

Conclusion

In this article, we discussed the limited data, the performance of several machine learning and deep learning algorithms, the amount of data increasing and decreasing, the type of problem that can occur due to limited data, and the common ways to deal with limited data. This article will help one understand the process of restricted data, its effects on performance, and how to handle it.

Some Key Takeaways from this article are:

1. Machine Learning and shallow neural networks are the algorithms that are not affected by the amount of data after some threshold level.

2. Deep neural networks are data-hungry algorithms that never stop learning from data.

3. Limited data can cause problems in every field of machine learning applications, e.g., classification, regression, time series, image processing, etc.

4. We can apply Data augmentation, imputation, and some other custom approaches based on domain knowledge to handle the limited data.

Want to Contact the Author?

Follow Parth Shukla @AnalyticsVidhya, LinkedIn, Twitter, and Medium for more content.

Contact Parth Shukla @Parth Shukla | Portfolio or Parth Shukla | Email to contact me.

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

Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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