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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

Top 5 Statistical Concepts Every Data Scientist Should Know in 2020!

pavan
pavan Last Updated : 25 Nov, 2020
6 min read

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

Must know Statistical concepts for the Data Science journey

The main goal of why I am here today to give a brief about my favorite statistical concepts that make your journey pleasant and lead you to success in the field of Data Science. There are many articles already, but I hope to make this more concise. Statistics is a powerful tool while performing the art of Machine Learning and Data Science. A basic visualization such as a box plot gives you some more useful information, and with the help of statistics, we can get more info and insights into data in a targeted way.

By using the statistical concepts, we get more useful insights that let us know the distribution of data and structure where we are able to apply more Machine Learning techniques to acquire more knowledge of data. Without further ado, let’s dive into it.

 

Probability Distribution

statistical concepts - Distribution

A Uniform distribution has a single value that occurs in a particular range while anything outside the range is just 0. We can assume it as a representation of categorical variables either 0 or 1. The categorical variable might have multiple values but we can visualize the same as a piecewise function of multiple uniform distributions.

A Normal Distribution is also known as Gaussian Distribution that is defined by its mean and standard deviation. The mean shifts distribution spatially where standard deviation controls the spread. We know the average value of our dataset and the spread of the data with Gaussian distribution.

A Poisson Distribution is the same as Normal but with the addition of skewness. It has a relatively uniform spread in all directions just like normal at the time of low-value skewness. The spread of data will be different in different directions when the skewness value is high.

There are many distributions you can know which help to interpret our categorical data with Uniform distribution. Many algorithms perform well with Gaussian. In Poisson, we have to take extra care and choose a robust algorithm.

Probability distributions helped calculate confidence intervals for parameters and critical regions for the hypothesis tests. For univariate data, it is useful to determine a reasonable distributional model for the data. Statistical intervals and hypothesis tests depended on specific distributional assumptions.

 

Over and Under Sampling

Classification problems use these techniques. Sometimes, our classification dataset is biased towards one side. For example, we have 1000 samples for class 1, but only 200 for class 2. We have ML techniques to model the data and make predictions. In this case, we have two preprocessing options that help in the training of our ML models.

Under-sampling means we select only some data from the majority class, as the same number of the minority classes. Now we have a balance on the probability distribution of the classes. The dataset is level out by choosing fewer samples.

Oversampling means we multiply the minority class such that it has the same count as the majority class. Now we have leveled out our dataset and the distribution of minorities without additional data.

Statistical Concepts - Undersampling and Oversampling

In the above-mentioned example, we can solve the issue in two ways. By using undersampling we select only 200 records for both the class 1 and 2. Another method is using oversampling or upsampling we replicate 200 examples to 800 such that both classes have 100 examples each where the model works better.

 

Accuracy

True positive: whenever the predicted and the original value is the same then it is true or counted.

True negative: doesn’t recognize or identify when the condition is not true.

False-positive: whenever the condition is not present it is false or detected.

False-negative: doesn’t identify the condition when it is not present.

Sensitivity: also termed as recall; measures the proportion of actual positive cases that got predicted as positive (True positive). sensitivity = TP/(TP+FN).

Specificity: measures the proportion of actual negative cases that got predicted negative (True negative). specificity = TN/(TN+FP).

Precision: measures the proportion of both True and False positive that got predicted correctly. precision = TP/(TP+FP).

Accuracy helps to evaluate the performance of models, and in some cases, it is not an efficient metric. Precision tells how precise/accurate our model is out of that predicted positive. When the costs of false-positive are high, Precision is a good measure to determine. In email spam detection, a false positive means that a non-spam email (actual negative) had shown as spam (predicted spam). The email user might lose important emails if the Precision is not high for the spam detection model. When there are high costs associated with false-negative, Recall is the best metric to choose the model. For example, in fraud detection. If a fraudulent transaction (Actual Positive) resulted as non-fraudulent (Predicted negative), the consequence would be worse for the bank.

 

Hypothesis Testing and Statistical Significance

Statistical Concepts - Hypothesis Testing

Null Hypothesis: the hypothesis that there is no difference between the specified population.

Alternative Hypothesis: The hypothesis that states something is happening to the sample observations due to an external cause.

P-value: it is the probability of obtaining the expected result of a test, assuming that the null hypothesis is true. A smaller p-value means there is stronger evidence in favor of the alternative hypothesis.

Alpha: the probability of rejecting the null hypothesis when it is true, this is also known as Type 1 error.

Beta: known as Type 2 error, failed to reject the false null hypothesis.

Out of all the statistical concepts, this is my favorite one. Hypothesis testing is an essential step in statistics. It helps to evaluate two mutually exclusive statements about a population to determine which argument is best supported by sample data. Statistical significance is a measure of the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the correct answer. A p-value of 5% or lower is considered statistically significant. Statistical hypothesis testing helps to determine whether the result of the data set is statistically significant.

 

Dimensionality Reduction

It is the process of reducing the dimensions of our dataset. The purpose of this is to solve problems that arise in the case of datasets with high dimensions. In other words, it has many features. When a more dependent variable exists in a dataset, then more samples need to have every combination of features that increase the complexity of our model. Dimensionality reduction can include less data equivalent to many features that help in faster computing, fewer redundancies, and more accurate models.

Dimensionality Reduction

From the above representation, we consider our dataset as a cubical structure that has three dimensions and 1000 points or values. With today’s computational power and techniques, 1000 records are easy to process, but on a large scale, we may run into problems. However, when we look at our data in a 2-Dimensional view that is one side of the cube, we can conclude that it’s easy to separate all the colors from this view. The projection of 3D data onto a 2D plane is possible by Dimensionality reduction. It effectively reduces the number of values we need to compute onto a 100. When it comes to vast data the reduction would be a more computational saving that leads to good results.

Future pruning is another way we can perform dimensionality reduction. In this, we remove features that are not important to our analysis. For example, after exploring our dataset we may find out of 9 independent variables, 6 of them have a high correlation with output, but the other 3 have a very low correlation. Then removing those three features doesn’t impact our results.

PCA is one of the most common statistical concepts used for Dimensionality reduction that creates vector representation of features that impact output that is a correlation.

 

Conclusion

Many other statistical concepts needed to be known for those who are interested in the journey towards Data Science based on the scenario. These are like building blocks of Data science. Statistics help to solve complex problems in the real world so that Data scientists and researchers can look for meaningful trends and modifications in data. In simple words, used to derive useful insights from data by performing mathematical computations on it. Hence, my advice is to spend enough time to learn skills that would be helpful in your journey.

That’s all for the day. I hope you would have found this article useful. Thank you.

pavan
pavan

Embarking on a transformative odyssey through the realms of AI, ML, and NLP, I've woven a tapestry of experience over three dynamic years. Amidst the digital symphony, I now find myself enraptured by the artistry of Generative AI, sculpting the future of innovation. As I dance with colossal language models, each keystroke becomes a brushstroke, painting the canvas of possibility in this ever-evolving technological landscape.

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