This slide is a part of the Introductory Machine Learning Lab Sessions that I'm currently conducting in this Spring 2018 semester at Daffodil International University. I hope this would be helpful for my students as well as other enthusiasts.
Using Bayesian Optimization to Tune Machine Learning ModelsScott Clark
1) Bayesian optimization can be used to efficiently tune the hyperparameters of machine learning models, requiring far fewer evaluations than standard random search or grid search methods to find good hyperparameters.
2) It builds a statistical model called a Gaussian process to model the objective function based on previous evaluations, and uses this to select the most promising hyperparameters to evaluate next in order to optimize an objective metric like accuracy.
3) SigOpt is a service that uses Bayesian optimization to tune machine learning models, outperforming expert humans on tasks like classifying images from CIFAR10 and reducing error rates more than standard methods.
Using Bayesian Optimization to Tune Machine Learning ModelsSigOpt
1. Tuning machine learning models is challenging due to the large number of non-intuitive hyperparameters.
2. Traditional tuning methods like grid search are computationally expensive and can find local optima rather than global optima.
3. Bayesian optimization uses Gaussian processes to build statistical models from prior evaluations to determine the most promising hyperparameters to test next, requiring far fewer evaluations than traditional methods to find better performing models.
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
This webinar, hosted by SigOpt co-founder and CEO Scott Clark, explains how advanced features can help you achieve your modeling goals. These features include metric definition and multimetric optimization, conditional parameters, and multitask optimization for long training cycles.
SigOpt CEO Scott Clark provides insights for modeling at scale in systematic trading. SigOpt works with algorithmic trading firms that collectively represent $300 billion in assets under management (AUM). In this presentation, Scott draws on this experience to provide a few critical insights to how these companies effectively model at scale. Alongside these insights, Scott shares a more specific case study from working with Two Sigma, a leading systematic investment manager.
This talk discusses the intuition behind Bayesian optimization with and without multiple metrics. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
BDW Chicago 2016 - Don Deloach, CEO and President, Infobright - Rethinking Ar...Big Data Week
All analysts and associated industry projections suggest the rate at which data volumes will grow continues to pick up momentum. While it may seem we are splitting at the seams now, projections suggest we are on the cusp of hitting the wall with current architectural models with no end in sight. When gaining insight from the data is a function of one or more complex queries, simply applying more hardware and more people becomes unfeasible.
In this presentation, Infobright CEO Don DeLoach will discuss how high-value approximation can be used to gain equivalent insight to exact queries while overcoming the prohibitive time and costs associated with continuing with traditional models.
Rethinking the problem using statistical metadata offers a compelling opportunity to overcome the mounting scale barriers by drastically reducing the resource requirements and query times to enable previously unattainable opportunities.
In this video I’m going to show you how SigOpt can help you amplify your machine learning and AI models by optimally tuning them using our black-box optimization platform.
Video: https://youtu.be/EjGrRxXWg8o
The SigOpt platform provides an ensemble of state-of-the-art Bayesian and Global optimization algorithms via a simple Software-as-a-Service API.
Using Bayesian Optimization to Tune Machine Learning ModelsScott Clark
1) Bayesian optimization can be used to efficiently tune the hyperparameters of machine learning models, requiring far fewer evaluations than standard random search or grid search methods to find good hyperparameters.
2) It builds a statistical model called a Gaussian process to model the objective function based on previous evaluations, and uses this to select the most promising hyperparameters to evaluate next in order to optimize an objective metric like accuracy.
3) SigOpt is a service that uses Bayesian optimization to tune machine learning models, outperforming expert humans on tasks like classifying images from CIFAR10 and reducing error rates more than standard methods.
Using Bayesian Optimization to Tune Machine Learning ModelsSigOpt
1. Tuning machine learning models is challenging due to the large number of non-intuitive hyperparameters.
2. Traditional tuning methods like grid search are computationally expensive and can find local optima rather than global optima.
3. Bayesian optimization uses Gaussian processes to build statistical models from prior evaluations to determine the most promising hyperparameters to test next, requiring far fewer evaluations than traditional methods to find better performing models.
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
This webinar, hosted by SigOpt co-founder and CEO Scott Clark, explains how advanced features can help you achieve your modeling goals. These features include metric definition and multimetric optimization, conditional parameters, and multitask optimization for long training cycles.
SigOpt CEO Scott Clark provides insights for modeling at scale in systematic trading. SigOpt works with algorithmic trading firms that collectively represent $300 billion in assets under management (AUM). In this presentation, Scott draws on this experience to provide a few critical insights to how these companies effectively model at scale. Alongside these insights, Scott shares a more specific case study from working with Two Sigma, a leading systematic investment manager.
This talk discusses the intuition behind Bayesian optimization with and without multiple metrics. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
BDW Chicago 2016 - Don Deloach, CEO and President, Infobright - Rethinking Ar...Big Data Week
All analysts and associated industry projections suggest the rate at which data volumes will grow continues to pick up momentum. While it may seem we are splitting at the seams now, projections suggest we are on the cusp of hitting the wall with current architectural models with no end in sight. When gaining insight from the data is a function of one or more complex queries, simply applying more hardware and more people becomes unfeasible.
In this presentation, Infobright CEO Don DeLoach will discuss how high-value approximation can be used to gain equivalent insight to exact queries while overcoming the prohibitive time and costs associated with continuing with traditional models.
Rethinking the problem using statistical metadata offers a compelling opportunity to overcome the mounting scale barriers by drastically reducing the resource requirements and query times to enable previously unattainable opportunities.
In this video I’m going to show you how SigOpt can help you amplify your machine learning and AI models by optimally tuning them using our black-box optimization platform.
Video: https://youtu.be/EjGrRxXWg8o
The SigOpt platform provides an ensemble of state-of-the-art Bayesian and Global optimization algorithms via a simple Software-as-a-Service API.
Tuning the Untunable - Insights on Deep Learning OptimizationSigOpt
This document discusses techniques for optimizing deep learning models, including hyperparameter optimization. It describes SigOpt's approach which uses software to automate repeatable tasks like training orchestration and model tuning. Experts can then focus on data science tasks. SigOpt utilizes techniques like Bayesian optimization, multitask optimization, and infrastructure orchestration to improve model performance while reducing costs and tuning time.
This document provides an overview of machine learning algorithms, including supervised and unsupervised learning algorithms. It discusses linear regression, boosted decision trees, factorization machines, sequence-to-sequence models for machine translation, image classification using ResNet, time series forecasting with DeepAR, K-means clustering, principal component analysis (PCA), and neural topic modeling. It also describes how these algorithms are implemented and optimized in Amazon SageMaker for performance and scalability.
Is This Thing On? A Well State Model for the PeopleDatabricks
The document discusses using machine learning models to determine well production state (on vs off) from sensor data. It presents an existing data architecture and issues with data quality. A supervised learning model is proposed using a decision tree trained on labeled rod pump production data. The modeling workflow includes data preprocessing, feature engineering, hyperparameter tuning and grid search. Decision trees are chosen for their interpretability but the document notes larger models may perform better. Overall production state modeling could help optimize operations and outperform existing controllers.
Training and tuning models with lengthy training cycles like those in deep learning can be extremely expensive and may sometimes involve techniques that degrade performance. We'll explore recent research on optimization strategies to efficiently tune these types of deep learning models. We will provide benchmarks and comparisons to other popular methods for optimizing the models, and we'll recommend valuable areas for further applied research.
Waking the Data Scientist at 2am: Detect Model Degradation on Production Mod...Chris Fregly
The document discusses Amazon SageMaker Model Monitor and Debugger for monitoring machine learning models in production. SageMaker Model Monitor collects prediction data from endpoints, creates a baseline, and runs scheduled monitoring jobs to detect deviations from the baseline. It generates reports and metrics in CloudWatch. SageMaker Debugger helps debug training issues by capturing debug data with no code changes and providing real-time alerts and visualizations in Studio. Both services help detect model degradation and take corrective actions like retraining.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
Weekly #105: AutoViz and Auto_ViML Visualization and Machine LearningBill Liu
https://learn.xnextcon.com/event/eventdetails/W20040310
I will describe what is available in terms of Open Source and Proprietary tools for automating Data Science tasks and introduce 2 new tools: one to visualize any sized data set with one click, another: to try multiple ML models and techniques with a single call. I will provide the Github Repos for both for free in the talk.
Computational visual system to reduce setup time in CNC vertical machining ce...Paulo Araujo
The presentation did at the XXXVIII Iberian Latin-American Congress on Computational Methods in Engineering regarding a technological approach to enhance machine setup efficiency through computer vision. For further information, please refer to this page: http://bit.ly/cilamce2017
How EVERFI Moved from No Automation to Continuous Test Generation in 9 MonthsApplitools
See and hear about EVERFI's journey to generating targeted tests automatically from changing system schemas in this webinar with Applitools, CircleCI, and Cypress. Greg Sypolt, VP of Quality Engineering, and Sneha Viswalingam, Director of Quality Engineering, share the time and productivity savings achieved through this approach, and how adopting shift-left test generation has shortened the QA cycle.
* See the Applitools products used, including the Ultrafast Grid, at https://applitools.info/n3o
* Read and download the case study at https://applitools.info/gbi
Metric Management: a SigOpt Applied Use CaseSigOpt
These slides correspond to a recording of a live webcast of a demo of Metric Management functionality in SigOpt, keeping model size down while increasing validation accuracy for a road sign image classification problem.
This document discusses Bayesian global optimization as a method for tuning machine learning models. It begins by outlining challenges with traditional tuning methods like grid search and random search. It then introduces Bayesian global optimization, which uses a Gaussian process model and expected improvement criterion to efficiently search the parameter space. The document provides examples of applying Bayesian optimization to deep learning tasks in MXNet and TensorFlow to achieve faster and better performance than traditional methods. It concludes by discussing tools for evaluating optimization strategies and comparing Bayesian optimization to baseline methods.
This document discusses Bayesian global optimization and its application to tuning machine learning models. It begins by outlining some of the challenges of tuning ML models, such as the non-intuitive nature of the task. It then introduces Bayesian global optimization as an approach to efficiently search the hyperparameter space to find optimal configurations. The key aspects of Bayesian global optimization are described, including using Gaussian processes to build models of the objective function from sampled points and finding the next best point to sample via expected improvement. Several examples are provided demonstrating how Bayesian global optimization outperforms standard tuning methods in optimizing real-world ML tasks.
Scott Clark, Co-Founder and CEO, SigOpt at MLconf SF 2016MLconf
Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications.
We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.
We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
The document discusses how to accelerate and amplify the impact of modelers. It describes SigOpt's platform which allows for automated hyperparameter optimization, tracking of experiments, and reuse of insights. This helps make modeling faster, cheaper, and better. The document advocates balancing flexibility and standardization, maximizing resource utilization through techniques like parallelization, and unlocking new capabilities such as optimizing expensive models or exploring architectures.
In this deck I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.
Comprehensive container based service monitoring with kubernetes and istioFred Moyer
The document provides an overview of using Kubernetes and Istio to monitor microservices. It discusses using Istio to collect telemetry data on requests, including rate, errors, and duration. This data can be visualized in Grafana dashboards to monitor key performance indicators. Histograms are recommended to capture request durations as they allow calculating percentiles over time for service level indicators. An Istio metrics adapter is also described that sends telemetry data to Circonus for long-term storage and alerting.
A workshop to demonstrate how we can apply agile and continuous delivery principles to continuously deliver value in machine learning and data science projects.
Code: https://github.com/davified/ci-workshop-app
Tuning the Untunable - Insights on Deep Learning OptimizationSigOpt
This document discusses techniques for optimizing deep learning models, including hyperparameter optimization. It describes SigOpt's approach which uses software to automate repeatable tasks like training orchestration and model tuning. Experts can then focus on data science tasks. SigOpt utilizes techniques like Bayesian optimization, multitask optimization, and infrastructure orchestration to improve model performance while reducing costs and tuning time.
This document provides an overview of machine learning algorithms, including supervised and unsupervised learning algorithms. It discusses linear regression, boosted decision trees, factorization machines, sequence-to-sequence models for machine translation, image classification using ResNet, time series forecasting with DeepAR, K-means clustering, principal component analysis (PCA), and neural topic modeling. It also describes how these algorithms are implemented and optimized in Amazon SageMaker for performance and scalability.
Is This Thing On? A Well State Model for the PeopleDatabricks
The document discusses using machine learning models to determine well production state (on vs off) from sensor data. It presents an existing data architecture and issues with data quality. A supervised learning model is proposed using a decision tree trained on labeled rod pump production data. The modeling workflow includes data preprocessing, feature engineering, hyperparameter tuning and grid search. Decision trees are chosen for their interpretability but the document notes larger models may perform better. Overall production state modeling could help optimize operations and outperform existing controllers.
Training and tuning models with lengthy training cycles like those in deep learning can be extremely expensive and may sometimes involve techniques that degrade performance. We'll explore recent research on optimization strategies to efficiently tune these types of deep learning models. We will provide benchmarks and comparisons to other popular methods for optimizing the models, and we'll recommend valuable areas for further applied research.
Waking the Data Scientist at 2am: Detect Model Degradation on Production Mod...Chris Fregly
The document discusses Amazon SageMaker Model Monitor and Debugger for monitoring machine learning models in production. SageMaker Model Monitor collects prediction data from endpoints, creates a baseline, and runs scheduled monitoring jobs to detect deviations from the baseline. It generates reports and metrics in CloudWatch. SageMaker Debugger helps debug training issues by capturing debug data with no code changes and providing real-time alerts and visualizations in Studio. Both services help detect model degradation and take corrective actions like retraining.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
Weekly #105: AutoViz and Auto_ViML Visualization and Machine LearningBill Liu
https://learn.xnextcon.com/event/eventdetails/W20040310
I will describe what is available in terms of Open Source and Proprietary tools for automating Data Science tasks and introduce 2 new tools: one to visualize any sized data set with one click, another: to try multiple ML models and techniques with a single call. I will provide the Github Repos for both for free in the talk.
Computational visual system to reduce setup time in CNC vertical machining ce...Paulo Araujo
The presentation did at the XXXVIII Iberian Latin-American Congress on Computational Methods in Engineering regarding a technological approach to enhance machine setup efficiency through computer vision. For further information, please refer to this page: http://bit.ly/cilamce2017
How EVERFI Moved from No Automation to Continuous Test Generation in 9 MonthsApplitools
See and hear about EVERFI's journey to generating targeted tests automatically from changing system schemas in this webinar with Applitools, CircleCI, and Cypress. Greg Sypolt, VP of Quality Engineering, and Sneha Viswalingam, Director of Quality Engineering, share the time and productivity savings achieved through this approach, and how adopting shift-left test generation has shortened the QA cycle.
* See the Applitools products used, including the Ultrafast Grid, at https://applitools.info/n3o
* Read and download the case study at https://applitools.info/gbi
Metric Management: a SigOpt Applied Use CaseSigOpt
These slides correspond to a recording of a live webcast of a demo of Metric Management functionality in SigOpt, keeping model size down while increasing validation accuracy for a road sign image classification problem.
This document discusses Bayesian global optimization as a method for tuning machine learning models. It begins by outlining challenges with traditional tuning methods like grid search and random search. It then introduces Bayesian global optimization, which uses a Gaussian process model and expected improvement criterion to efficiently search the parameter space. The document provides examples of applying Bayesian optimization to deep learning tasks in MXNet and TensorFlow to achieve faster and better performance than traditional methods. It concludes by discussing tools for evaluating optimization strategies and comparing Bayesian optimization to baseline methods.
This document discusses Bayesian global optimization and its application to tuning machine learning models. It begins by outlining some of the challenges of tuning ML models, such as the non-intuitive nature of the task. It then introduces Bayesian global optimization as an approach to efficiently search the hyperparameter space to find optimal configurations. The key aspects of Bayesian global optimization are described, including using Gaussian processes to build models of the objective function from sampled points and finding the next best point to sample via expected improvement. Several examples are provided demonstrating how Bayesian global optimization outperforms standard tuning methods in optimizing real-world ML tasks.
Scott Clark, Co-Founder and CEO, SigOpt at MLconf SF 2016MLconf
Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications.
We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.
We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
The document discusses how to accelerate and amplify the impact of modelers. It describes SigOpt's platform which allows for automated hyperparameter optimization, tracking of experiments, and reuse of insights. This helps make modeling faster, cheaper, and better. The document advocates balancing flexibility and standardization, maximizing resource utilization through techniques like parallelization, and unlocking new capabilities such as optimizing expensive models or exploring architectures.
In this deck I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.
Comprehensive container based service monitoring with kubernetes and istioFred Moyer
The document provides an overview of using Kubernetes and Istio to monitor microservices. It discusses using Istio to collect telemetry data on requests, including rate, errors, and duration. This data can be visualized in Grafana dashboards to monitor key performance indicators. Histograms are recommended to capture request durations as they allow calculating percentiles over time for service level indicators. An Istio metrics adapter is also described that sends telemetry data to Circonus for long-term storage and alerting.
A workshop to demonstrate how we can apply agile and continuous delivery principles to continuously deliver value in machine learning and data science projects.
Code: https://github.com/davified/ci-workshop-app
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)GiselleginaGloria
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
2. Contents
What is Machine Learning
Supervised, Unsupervised, Reinforcement
Learning
Training, Test, Validation data
Basic Workflow of any ML Project
Cognota.ai
13. Train,Validation,
Test
(Tutorial)
Cognota.ai
Data
1st Split Data Test Data
Training Data Validation Data
Training (60%) Validation (20%) Test (20%)
Train your model to
fit the parameters
Tune the hyper
parameters of your
model, avoid over
fitting, choose
model
Test your model to
determine accuracy
and performance
15. Cause
&
Prevention
AVOID OVER FITTING
Happens when number of features is much higher than
number of training examples
Increase number of training data (sometimes might not
work)
Use Regularization (will talk later about this)
AVOID UNDER FITTING
When number of features is much lower than number of
training examples
Increase number of features
Might use compound features, or can go to a higher
dimensional feature space using Kernels (will discuss later)
Cognota.ai
16. Machine
LearningProject
Framework
Cognota.ai
1. Data Import
2. Data Preprocessing
3. Feature Engineering and Extraction
4. Model Selection
5. Train Model with Training Data
6. Tune the Hyper Parameters with Validation
Data (learning rate, regularization parameter,
number of layers in Neural Network etc.)
7. Execute Model on Test Data
8. Performance Evaluation
9. Go Back to step 3 and repeat and until
satisfactory accuracy achieved
10.Conclude