#metrics regression machinelearning

[teguhteja]

Model Evaluation: Mastering Performance Metrics and Selection in Machine Learning

Dive into the world of model evaluation in machine learning! Discover how to optimize logistic regression, compare ensemble techniques, and select the best model using key performance metrics. #MachineLearning #ModelEvaluation #DataScience

Model evaluation is a crucial step in the machine learning pipeline. In this comprehensive guide, we’ll explore how to master performance metrics and make informed model selections to ensure your machine learning projects succeed. The Importance of Post-Optimization Model Evaluation When it comes to machine learning, model evaluation goes beyond simple accuracy comparisons. To truly understand…

[optimisticjellyfishheart-blog]

DESTINATION : Regression Metrics

ROUTE :  Mean Absolute Error (MAE) - MAE is the average of the difference between the Original Values and the Predicted Values (known as residuals). It shows how far the predictions are from the actual output. Small MAE suggests the model is great at prediction, while a large MAE suggests that your model may have trouble in certain areas. It doesn’t indicate the direction of error – under predicting or over predicting. 

Mean Squared Error (MSE) - MSE takes average of square of the difference between the original values and predicted values (known as residuals). It is easy to compute the gradient. As we take square of the error, the effect of large errors becomes more pronounced than smaller errors.

Example :  # Evaluate model using MSE metric 

from sklearn.metrics import mean_squared_error y_pred = model.predict(X_test) mse = mean_squared_error(y_test,y_pred) print(f"Mean Squared Error : {mse:0.2f}")

R Squared (R2 Score) - R Squared is also known as coefficient of determination, represented by R2 or r2 and pronounced as R Squared. It is the number indicating the variance in the dependent variable that is to be predicted from the independent variable. R-squared is a statistical measure of how close the data are to the fitted regression line. R-squared = (Variation of mean  - Variation from fit line) /  Variation of mean. More specifically, R-squared gives you the percentage variation in y explained by x-variables. The range is 0 to 1 (i.e. 0% to 100% of the variation in y can be explained by the x-variables. When it is 0.91, it means there is 91% relationship between dependent and independent variables.

Example : # Evaluate model using R Squared 

from sklearn.metrics import r2_score y_pred = model.predict(X_test) r2score = r2_score(y_test,y_pred) print(f"R2 Score: {r2score:0.2f}")

[eurekakinginc]

"[D]Discussion about benchmarking autoML algorithms"- Detail: Hi, everyone on the r/MachineLearning,​Our lab wants to build a benchmark platform to test the autoML algorithms. We have several datasets and test the generated networks on different tasks(classification, regression etc.).​The question is, how to efficiently and fairly compare different algorithms? For example, we have picked AUC, RMSE. For real applications, users might not wait these algorithms until they converges: easy stopping is the most case. So we think the "speed" and “Model Capacity” are also important for the benchmark platform. (we have not decided to use which metrics for them). Any other indicators you guys think meaningful and useful?​Do you have any idea or suggestion about it? Or anything the customer(anyone who wants to pick an autoML algorithm by giving the benchmark results) might care? Thanks:). Caption by tradigrada. Posted By: www.eurekaking.com

[oodlestech]

DevOps and Machine Learning are the technologies that are creating a combined impact on the software industry today.

As the DevOps engineers are expected to understand the working nature of the codes, infrastructure, cloud, and, other things, incorporation of machine learning into their work culture seems to be more promising.

Technologies today are pushing the boundaries that mankind would have ever dreamt of. Though there is no real substitute for intelligence and hard work, we witness a positive impact in businesses today with robotics, machine learning, artificial intelligence; evolving software development cultural practices and trends like DevOps, blockchain development, and so forth.

DevOps [truncated term for Development and Operations] is a bold new strategy getting implemented by the IT industries who are in search of the outcome-based model. All of them carry the main objective that is, create, test, release, and support the software at a reliable, scalable, and faster rate, that are closely aligned with the business objectives.

On the other hand, Machine Learning, an algorithm category, facilitates the data-driven automation for applications and predict the outcomes more accurately. It is able to receive the data input, analyze the statistics, predict the output, update the data, and, so forth. It is pushing the businesses to explore and implement data analysis model.

However, the DevOps advantage, when combined with harnessing, monitoring, and, analysis of the data it creates, is forecasted to help the team to optimize the operations in a better manner.

The potential of Machine Learning to make the DevOps smarter is the talk of the techno-town today and is becoming the key area to explore in the future.

In brief, the DevOps methodologies and machine learning are parallelly evolving and is logically considered for a successful outcome in combination.

Here is a brief note about how both these giant technologies could be implemented in combination for deriving better benefits in business.

How Machine Learning Benefits DevOps Methodology?

Till date, most of the teams depend on threshold monitoring approach wherein conventional habit and wisdom are the factors to rely on. This has resulted in a high signal-to-noise ratio and alert fatigue.

But, when you consider machine learning, it is of mathematical, statistical, and logical. That is, it is more grounded as it uses the models namely linear and logistic regression, deep learning, classification, and, etc., to scan the data sets, trends, correlations, and make the logical predictions.

You may also like: An Online Platform For Deploying Machine Learning Models

Optimizing DevOps with Machine Learning:

Machine Learning Application may analyze all the large data in store and help for predictive analysis.

The possibilities of looking at data in many forms and combinations are easier like velocity, bugs, metrics, continuous integration system, and, etc.

It delivers the data as per the needs like daily, weekly, monthly, or so; and trends like seasons, festivals, geography, and, etc., for gaining a complete insight.

The collection of data and its analysis helps to rule out the root causes, investigate the failure, and other issues easily.

It determines the efficiency of the orchestration and helps to analyze the tools and processes for more efficient and directed planning.

It helps to optimize a specific metric like maximizing the uptime, reduce the deployment time, maintain the standard performance, and, etc.

Advantages of Machine Learning in DevOps Methodology:

There are varied illustrations, where machine learning could be effectively implemented to DevOps. A few of them are mentioned below.

1. Machine learning implication on DevOps tools like Jira, Jenkins, Puppet, and, etc., identifies the software wastes like inefficient resources, partial work, task switch, process slow down, and other shortfalls that are faced during application delivery.

2. It helps to review the QA results, detect the errors, ensure proper testing, and thus raise the quality standard of the deliverables.

3. It helps to ensure the security of the applications by detecting the backdoors in coding, deployment of unauthorized code, theft against intellectual property rights, and etc., based on user behaviors and exercising patterns.

4. It analyzes and detects the abnormal pattern in usage, group the alerts based on transaction ID, servers, subnet, and, etc., enabling filtration. This, in turn, manages the alerts by reducing the alert storms and fatigue.

5. The machine learning tools can detect the anomalies in the process, operations, alerts, and suggests best-fixing measures. It automatically detects and also triages the known issues in a more intelligent manner.

6. It analyzes user metrics and its impact on the business at an early stage itself. For instance,

Cart abandonment, click through rates, user registrations, and, etc. This serves as an early warning for the businesses to take effective measures wherever needed.

Challenges of Machine Learning in DevOps:

It is understood that the machine learning tool implementation is still limited owing to several challenges. A few of them are highlighted below.

We find technical challenges among the DevOps practitioners as there is the need for better understanding about these technologies like machine learning, Artificial intelligence, and/or the predictive analysis. Example: Logarithms, statistics, linear algebra, programming, trigonometry, and, etc.

Further, implementation of machine learning itself is a challenge at the organizational level. Integration of it with DevOps is not yet understood completely and the management is not unanimous to support the decision.

The Hiring of a new team, encouraging the existing team to meet the DevOps and Machine learning challenges by upgrading themselves is still in the process.

What Next?

The effectiveness of machine learning is dependent on DevOps processes. By understanding the benefits a machine-learning DevOps infrastructure could provide to the business processes, and its implementation is anticipated to bring success in project management.

If the integrations are correlated and evaluated efficiently, the technology is going to create wonders in business processes positively.

It is expected that the proliferation of the frameworks would make the algorithms easier than the present scenario. As more professionals are expanding their skills in machine learning, we may expect more use cases in the near future.

Machine learning is able to map the best possible patterns for enhanced performance and infrastructure.

Thus, the implementation of machine learning in DevOps is highly recommended today.

Read more info at — https://www.oodlestechnologies.com/blog/tagged/MachineLearning

[towardsai]

Evaluation Metrics for Regression Problems Author(s): Edward Ma Why metrics need to be defined at the very beginningContinue reading on Towards AI — Multidisciplinary Science Journal » Published via Towards AI #MachineLearning #ML #ArtificialIntelligence #AI #DataScience #DeepLearning #Technology #Programming #News #Research #MLOps #EnterpriseAI #TowardsAI #Coding #Programming #Dev #SoftwareEngineering https://towardsai.net/p/data-science/evaluation-metrics-for-regression-problems?utm_source=facebook&utm_medium=social&utm_campaign=rop-content-recycle #datascience

[towardsai]

Regression and Classification Metrics in Machine learning with Python Author(s): Amit Chauhan Model evaluation with metric API for regression and classificationContinue reading on Towards AI » Published via Towards AI #MachineLearning #ML #ArtificialIntelligence #AI #DataScience #DeepLearning #Technology #Programming #News #Research #MLOps #EnterpriseAI #TowardsAI #Coding #Programming #Dev #SoftwareEngineering https://bit.ly/3BAJNXK #machinelearning

[towardsai]

Evaluation Metrics for Regression Problems by Edward Ma via @Towards_AI → https://bit.ly/3gSmsb8 #MachineLearning #ML #ArtificialIntelligence #AI #DataScience #DeepLearning #Technology #Programming #news #research #TowardsAI #Science ⊕ [ Link on Bio ] ⊕