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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

Deterministic vs Stochastic – Machine Learning Fundamentals

Y
Yana Khare Last Updated : 05 Jul, 2024
5 min read

Introduction

Deterministic and stochastic models are approaches in various fields, including machine learning and risk assessment. Understanding the differences between these models is crucial for making informed decisions and predictions. This article will explore the pros and cons of difference between deterministic and stochastic models, their applications, and their impact on machine learning and risk assessment.

If you’re eager to delve into the machine learning models, consider exploring Analytics Vidhya’s Blackbelt+ Program, where you can gain hands-on experience and unlock the full potential of these transformative technologies. 

Learning Objectives:

  • Understand the fundamental differences between deterministic and stochastic models.
  • Learn about the pros and cons of using stochastic vs deterministic models.
  • Explore the applications of stochastic vs deterministic models in machine learning and risk assessment.
  • Recognize the factors that influence the choice in difference between deterministic and stochastic models, such as data requirements, assumptions, and predictability.

Table of contents

Understanding Deterministic and Stochastic Models

Deterministic models are based on precise inputs and produce the same output for a given set of inputs. These models assume that the future can be predicted with certainty based on the current state. 

On the other hand, stochastic models incorporate randomness and uncertainty into the modeling process. They consider the probability of different outcomes and provide various possible results.

Pros and Cons of Deterministic Models

Deterministic Models

Pros:

  • Deterministic models establish a transparent cause-and-effect relationship between inputs and outputs, facilitating a more straightforward interpretation.
  • Deterministic models are computationally efficient, requiring less processing power than their stochastic counterparts.
  • These models require less data for accurate predictions, which makes them suitable for scenarios with limited data availability.

Cons: 

  • Deterministic models assume that we know and can accurately measure all variables, a condition that may not always align with real-world complexities.
  • They do not account for uncertainty and randomness inherent in many real-world situations, leading to potential inaccuracies in predictions.

Pros and Cons of Stochastic Models

Pros:

  • Stochastic models consider uncertainty and randomness, rendering them well-suited for scenarios characterized by unpredictable futures.
  • They offer a range of possible outcomes, enabling decision-makers to evaluate the likelihood of various scenarios and make informed choices.

Cons: 

  • Stochastic models demand more extensive data and computational resources than deterministic models, which may be a constraint in resource-limited situations.
  • Due to the probabilistic nature of their outputs, stochastic models can be more complex to interpret, requiring a nuanced understanding of probability and statistical concepts.

Differences Between Deterministic and Stochastic Models

Deterministic V/S Stochastic

Definition and Concept

Deterministic models are based on fixed inputs and produce the same output every time. They assume that the future can be precisely determined based on the current state. On the other hand, stochastic models incorporate randomness and uncertainty into the modeling process. They consider the probability of different outcomes and provide various possible results.

Use Cases and Applications

People commonly use deterministic models in scenarios with well-defined and predictable inputs and outputs. For instance, engineers and physicists often employ deterministic models to analyze the behavior of systems with known parameters.

Conversely, individuals use stochastic models in situations where the future is uncertain and unpredictable. These models have wide applications in finance, weather forecasting, and risk assessment.

Assumptions and Predictability

Deterministic models assume that all variables are known and can be accurately measured. They rely on precise inputs and produce deterministic outputs. Stochastic models, on the other hand, consider uncertainty and randomness. They incorporate probabilistic inputs and have a range of possible outputs, allowing decision-makers to assess the likelihood of different outcomes.

Data Requirements and Availability

Deterministic models require less data compared to stochastic models. Since they rely on precise inputs, deterministic models can be trained and validated with a smaller dataset. Stochastic models, on the other hand, require a larger dataset to capture the randomness and variability in the system. They rely on statistical techniques to estimate the parameters and make predictions.

Deterministic vs Stochastic in Machine Learning

In machine learning, both deterministic and stochastic models play a crucial role. Deterministic machine learning algorithms, such as linear regression and decision trees, aim to find a fixed relationship between inputs and outputs. They provide interpretable models and are often utilized in scenarios where the data behaves predictably.

Stochastic machine learning algorithms, such as neural networks and random forests, incorporate randomness and uncertainty into the modeling process. They capture complex patterns and relationships in the data, making them suitable for uncertain future scenarios. Stochastic algorithms often outperform deterministic algorithms in image recognition and natural language processing tasks.

Performance and Accuracy Comparison

The performance and accuracy of stochastic vs deterministic models depend on the specific problem and dataset. Deterministic models excel in scenarios where the inputs and outputs have a clear cause-and-effect relationship. They provide interpretable models and can make accurate predictions when the underlying assumptions are met.

Stochastic models, on the other hand, excel in scenarios where the future is uncertain and unpredictable. They capture the variability and randomness in the data, allowing decision-makers to assess the likelihood of different outcomes. Stochastic models can provide more accurate predictions when the underlying assumptions of randomness hold.

Stochastic vs Deterministicin Risk Assessment

Deterministic risk assessment involves analyzing the potential risks and their impacts based on fixed inputs and assumptions. It provides a deterministic estimate of the risks and helps decision-makers understand the possible consequences of different actions. Deterministic risk assessment is commonly used in fields such as insurance and finance.

On the other hand, stochastic risk assessment incorporates randomness and uncertainty into the risk analysis process. It considers the probability of different outcomes and provides a range of possible risks. Stochastic risk assessment helps decision-makers understand the likelihood of different scenarios and make informed decisions based on the level of uncertainty.

Robustness and Uncertainty Analysis

Deterministic risk assessment analyzes the risks based on fixed inputs and assumptions. It provides a deterministic estimate of the risks and their impacts. However, deterministic risk assessment does not account for uncertainty and variability, leading to inaccurate predictions and decisions.

On the other hand, stochastic risk assessment incorporates randomness and uncertainty into the analysis. It considers the probability of different outcomes and provides a range of possible risks. Stochastic risk assessment helps decision-makers understand the robustness of their decisions and assess the impact of uncertainty on the outcomes.

Conclusion

Deterministic and stochastic models offer different approaches to modeling and prediction. Deterministic models provide a clear cause-and-effect relationship between inputs and outputs, making them easier to interpret. However, they have limitations in capturing uncertainty and randomness. Stochastic models, conversely, consider uncertainty and provide a range of possible outcomes. They are suitable for scenarios where the future is unpredictable. Understanding the pros and cons of deterministic and stochastic models is crucial for making informed decisions and predictions in various fields, including machine learning and risk assessment.

Key Takeaways:

  • Deterministic models assume a fixed cause-and-effect relationship between inputs and outputs, while stochastic models incorporate randomness and uncertainty.
  • Deterministic models are computationally efficient and require less data but may oversimplify real-world complexities.
  • Stochastic models account for uncertainty but demand more data and computational resources.
  • In machine learning, deterministic algorithms excel in scenarios with clear input-output relationships, while stochastic algorithms capture complex patterns and uncertainty.

Frequently Asked Questions

Q1. What is the difference between determinism and stochastic?

A. Determinism implies outcomes are precisely determined by initial conditions without randomness, while stochastic processes involve inherent randomness, leading to different outcomes under identical conditions.

Q2. What is an example of stochastic?

A. An example of a stochastic process is stock market prices, where daily fluctuations are influenced by numerous unpredictable factors, leading to random changes.

Q3. What is the difference between deterministic and stochastic error?

A. Deterministic error is consistent and predictable, arising from systematic biases. Stochastic error is random and unpredictable, caused by inherent variability in data or processes.

Q4. What is an example of a deterministic system?

A. An example of a deterministic system is a simple pendulum’s motion, which can be precisely predicted using its initial conditions and physical laws, without randomness.

DeterministicDeterministic vs Stochasticmachine learningMachine Learning FundamentalsStochasticstochastic vs deterministic
Y
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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