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Reading list

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

How Machine Learning Models Fail to Deliver in Real-World Scenarios

Eshan
Eshan Last Updated : 05 Oct, 2020
5 min read

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

Introduction

Yesterday, my brother broke an antique at home. I began to search for FeviQuick (a classic glue) to put it back together. Given that it’s one of the most misplaced items, I began to search for it in every possible drawer and every untouched corner of the house I hadn’t been to in the past 3 months. I gave up the search after an hour – the FeviQuick was nowhere to be found. I narrowed down the places to look and began to search again, only to find it pressed under three books!

But what does this story have to do with machine learning models? Let me explain how I thought through this versus how a machine learning model will think it through.

 

The Human Mind vs. The Machine – Problem Solving

I would make a list of all the possible places it could be (data). My brain would automatically prioritize which places to search thoroughly. This could be considered as assigning a probability to each of the places (prior). Without yielding any results after my first search, I started thinking about all the probable places. My mother thought she had last seen it in my bedroom (new data). I now thought about the likelihood of where the object could be found, given it was in my bedroom (Mind).

In this whole thinking process, my mind is assigning new probabilities to each of these places (posteriori). Basically, my value of probability is not constrained to one value, but a range of values. This is exactly how Bayesian probabilities are ranged – not a single value, but a distribution of values.

Source: blog.datumbox.com

Now for the machine. We input the data containing all the places it was to possibly find it. Each place of finding the object would be equally likely for the model, as the machine knows no such thing called bias, nor does it have any prior training data to refer to.

Now that the machine is unable to get any result, hence it assigns zero value to each of the probabilities present. Even if we taught the machine to only consider a few places, it would give each place an equally likely probability that would not help our case. Here, we can see that the values of the probability are confined to a single value. The machine assigns the value zero if the object isn’t found, as it gives no room to the probability that it could’ve made an error (all machines are prone to error ).

 

There’s no space for the ML model to incorporate uncertainty and probability!

 

Machine Learning in the Real World

Consider this being applied to a real-life problem, where there has been a kidnapping and we try to narrow down the possibilities where the kidnapper could’ve taken the person.

This is the Prior knowledge we may have, and hence depicting the likelihood of finding the kidnapper given he is in that square.

As humans, we know that no place can be ruled out because first of all, the kidnapper need not be stationary and there is always a possibility that he could be hidden in a place we have no whereabouts of. Hence, we make a probability of where he could be, based on the type of buildings present at each block. A higher probability has been given to those areas which are shady and remotely located. This is called the likelihood.

Now, let’s say we get a tip from a person that he surely saw the person go straight from E13 block ahead? Then our probabilities would change as follows:

The Posterior Probability of where the kidnapper could be.

Here, we can see that there has been a shift in the alignment of probabilities. The cells towards the right of block E12 have gotten a rise in the green shade. This is also sometimes referred to as the posterior probability. The likelihood has been multiplied with a value E such that the posterior value of the probability has been updated in those regions. This value E is called the evidence.

Despite the information, it has not assigned zero probability to the blocks before E12, because it does take uncertainty into account. 

This hierarchy can be applied as a chain of events, with the evidence being updated as soon as new information is fed to it. The posterior for the previous situation will become the likelihood for the updated situation. Let us give a formula to what we’ve been visualizing:

Here, P(A1|B1), P(A2|B2) are the calculated posteriors

For a machine learning model, such as logistic regression, or even for a multi-layer perceptron, it would be impossible to train these models without considering the Bayesian analogy. For a machine learning model, it would assign values that would look something like this:

An equally likely Probability assigned to each block

Now, let us see how a machine learning model would react to new information being added as before, i.e the kidnapper had been sighted going towards the right of E12:

Here the model has very less representation and shows too much bias and dependency on the data

Can you see how biased the model has become? It has straightaway assigned a value of zero to places before E12. This seems rather inconvenient as it does not take into account the factors that the information or data provided might be wrong, hence the model might already be looking in the wrong direction and wasting time.

This is because the models follow the frequentist approach, where, if the item is not found, its probability will be zero regardless of any other conditions specified (the kidnapper may have traveled to the left of block E12 or the informant may be wrong).

 

End Notes

So, the Bayesian approach is one that has a personal perspective. This application can prove to be very important in the real-world and the personal assumptions we make are all deemed right if they’re done in a proper mathematical environment.

The frequentist approach, no matter how statistically strong it might be, just cannot take on real-life applications because it has no space for uncertainty as well as the unpredictability of the probability of an outcome.

Since neural networks and machine learning models do not incorporate Bayesian probability, A/B tests and real-world situations often make use of Monte Carlo simulation and Markov models.

Eshan
Eshan

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