Technical Analysis’ Post

Evaluating machine learning models is crucial for real-world success! 🌍📊 In our latest article, we explore the importance of robust evaluation techniques like cross-validation, holdout validation, and bootstrapping. 🧪🔍 Key insights include: 1️⃣ Effective data splitting

Machine learning models can only go so far with a limited dataset. That's where data augmentation comes in, helping create diverse, robust models by transforming existing data in smart ways.

Why is accuracy not an appropriate metric for evaluating many models? In the article below, I have explored this topic and introduced other metrics. This article is at a beginner level. I’d be happy to hear your thoughts! https://lnkd.in/e7zxX72r

Train and understand regression models in machine learning - https://lnkd.in/gTuf2tTU Regression is arguably the most widely used machine learning technique, commonly underlying scientific discoveries, business planning, and stock market analytics. This learning material takes a dive into some common regression analyses, both simple and more complex, and provides some insight on how to assess model performance. Learning objectives In this module, you will: Understand how regression works. Work with new algorithms: Linear regression, multiple linear regression, and polynomial regression. Understand the strengths and limitations of regression models. Visualize error and cost functions in linear regression. Understand basic evaluation metrics for regression. Prerequisites Familiarity with machine learning models https://lnkd.in/gTuf2tTU

Train and understand regression models in machine learning - https://lnkd.in/gTuf2tTU Regression is arguably the most widely used machine learning technique, commonly underlying scientific discoveries, business planning, and stock market analytics. This learning material takes a dive into some common regression analyses, both simple and more complex, and provides some insight on how to assess model performance. Learning objectives In this module, you will: Understand how regression works. Work with new algorithms: Linear regression, multiple linear regression, and polynomial regression. Understand the strengths and limitations of regression models. Visualize error and cost functions in linear regression. Understand basic evaluation metrics for regression. Prerequisites Familiarity with machine learning models https://lnkd.in/gTuf2tTU

We often encounter issues of Over-fitting and Under-fitting when building machine learning models. Selecting the right model and adjusting its complexity are key factors in helping the model avoid these problems. A model that is too simple may not be capable of learning and capturing all the features of the data, leading to Under-fitting. Conversely, a model that is too complex may learn the details of the training data too well, including noise, resulting in Over-fitting. Therefore, adjusting the size of the model, choosing the appropriate algorithm, and using techniques such as Regularization and Cross-validation can help the model strike a balance between complexity and generalization, thereby improving its performance on real-world data

How to use data clean rooms and machine learning to unlock significant effectiveness and efficiency

To select the right machine learning algorithm for your problem, spend time learning: 1. the nature of problem all algorithms supports 2. data characteristics each algorithm works best with 3. and the assumptions each algorithm makes

How to use data clean rooms and machine learning to unlock significant effectiveness and efficiency

Check out our blog post about " How to Use Machine Learning to Simplify Literature Monitoring"https://wix.to/K9A1J4a #newblogpost #Datacreds #Crypta #DrugSafetyDatabase