Your machine learning models are underperforming. What's the best software to improve them?

There's no software to improve an underperforming ML model 🤷🏼♂️ You'll need to roll up your sleeves and analyze what's causing the situation: - It can be having little, or bad quality data - It can be a poor hyperparameter tuning or training process - It can be some kind of data drift, causing your once very precise model to have lost performance in prod over time - And it can even be that the problem was ill-defined from the start and you need to re-assess your business needs and start over from there! In any case: all of these can be found and addressed with enough care and a robust methodic approach :)

Selecting the right software is crucial in machine learning. TensorFlow offers scalability for enterprise applications, while PyTorch provides flexibility for research. Scikit-learn is great for basic algorithms. In my experience at SAS, integrating SAS Viya has been transformative. Its robust analytics and user-friendly interface cater well to business environments, bridging advanced analytics with practical needs. It excels in data preparation, model building, and deployment, especially in collaborative and scalable enterprise settings. Matching the software's capabilities with project goals, whether TensorFlow, PyTorch, or SAS Viya, is key to unlocking the full potential of machine learning models.

Start with TensorFlow or PyTorch 🤖 for sophisticated modeling. Improve data quality with Pandas and NumPy 📊, and fine-tune with Optuna or Hyperopt 🎯. Remember, success hinges on high-quality data, selecting the right model, and meticulous tuning 🔍. Keep up with the latest in ML research 📚, and use GitHub for collaboration and version control 🤝. Embrace a persistent, curious mindset 🌟. Tools are only as effective as the strategy and understanding behind them. So, dive deep into your data, understand your model's nuances, and always keep learning and experimenting 🚀💡🔧.

I would always suggest using PowerPoint. Since most results ultimately get communicated using powerpoint, that is the best option to be able to manually adjust the metrics you'd like to report. The problem with TF, PyTorch, sklearn, etc. is that the error metrics tend to be built in and outputs of functions. With powerpoint, there's no such thing, so if you want to improve your metrics then its as simple as highlighting the correct text box and typing in a higher number (or lower if reporting loss).

The software to improve underperforming machine learning models includes TensorFlow, PyTorch, scikit-learn, Keras, and Apache Spark MLlib These tools provide a rich set of capabilities for data preparation, model training, and model assessment. Additionally, XGBoost is commonly used for tree-based model building and offers tools for model tuning and optimizatio It's important to carefully select the right software based on the project goals and requirements. However, it's essential to note that improving model performance also involves meticulous data validation, model evaluation, and iterative refinement, which are crucial steps in the process.

Model Performance in Machine Learning is all about how well the model accomplishes it envisioned tasks. Performance of the Machine Learning models can be measured by Classification and Regression Metrices like 1. Area Under Curve – ROC 2. Accuracy 3. Precision 4. Logarithmic Loss 5. Recall 6. F-Score 7. False Positive rate 8. Coefficient of determination 9. Mean Squared Error 10. Mean absolute Error etc. If models are underperforming, building a better data set is one of the options. As compared to Wider and longer data, we should concentrate on quality data which is less noisy and better sampled. Other important thing to consider are: 1. Use a Model Baseline 2. Make a retrainable model. 3. Data set and Model may be segregated.

Where does your data origin from? Is it from a lot of different sources with presumably differing qualtiy? How are the labels distributed across your data? What does unsupervised learning already tell you about the data? Which statistical measures are you using for model validation? How much does your validation set differ from the training data? You first need to find thorough answers for the above questions before diving into things like hyperparameter optimisation, choice of the learner etc..

- Feature engineering to create new features from existing ones or transforming existing features to make them more informative for the model. Techniques include scaling features, encoding categorical variables, creating interaction terms, and deriving new features from domain knowledge. - Identify and select the most relevant features for your model to improve efficiency and reduce overfitting. - Techniques include univariate feature selection, recursive feature elimination, and feature importance scores from tree-based models.

Optimizing machine learning models starts with refining the data. High-quality, diverse datasets are crucial. Tools like Pandas and NumPy aid in data manipulation, while Matplotlib and Seaborn enhance analysis through visualization. Beyond tool selection, it's about understanding the data's narrative – its distribution, anomalies, and patterns. Data augmentation techniques like cropping and rotating further enrich this dataset, offering a more comprehensive training ground for your models. As an ML expert, I emphasize on not just collecting data but curating it, ensuring that every data point contributes meaningfully to the model's learning and accuracy.

In order to optimize the data several techniques can be used like Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions. Term Frequency - Inverse Document Frequency (TF-IDF) is a widely used statistical method in natural language processing and information retrieval. It measures how important a term is within a document relative to a collection of documents.

The first thing to always look at is the data and how it is structured. It must be best presented for the model to read whether it is through data cleaning, feature engineering or ensemble learning. After that, it’s a matter of an iterative process and seeing which algorithms have good performances on the model. This can range from a simple linear regression all the way up to a transformer algorithm.

Once we are super convinced that data is good, checking different models is important. Here Occum's razor comes in. That is when in comparison, the simplest is the best. we check the validation metrics with models like XGBoost and we get good results and we get satisfied with them. This is where things go wrong. We put the model in production and the model can't handle real world data well. So, we need to experiment with different models, keeping not only the validation metrics but how is model performing on training data compared to other validation samples. Here I would also go a step ahead and do cross validation, compare with multiple validation sets. Then you need to come to conclusion.

Your model performance varies significantly based on the algorithms you use with specific types of data. Therefore, experiment and find out what fits the best for your data and how you can get the best results. You can also do hyperparameter tunning and use different parameters on the same model to make it fit better.

Exploring diverse algorithms such as decision trees, support vector machines, and neural networks is essential. Assess how each aligns with your dataset characteristics and objectives to determine the most suitable approach for achieving optimal model performance.

I have found that as part of the core modeling process, eschewing dependence on any single model in favor of training multiple models and combining predictions may work out better in the long run.

The best way to overcome this is by using your validation set to tune the hyperparameters. Many students make the mistake of not having a validation set. They only have the train and test set. Having a validation set helps a lot in tuning the hyperparameters. Grid search is a great way to land at an optimal set.

Tuning the hyper parameters is an important step in this process. Being patient and trying out all possible combinations of hyper parameters will some time influence the model performance. It is a very long and time consuming process but I have seen an improvement of even 10 percent by doing this.

Tuning the hyperparameters can really improvise the model performance. Scikit-learn is a machine learning library in Python that provides tools for hyperparameter tuning.It includes functions like GridSearchCV and RandomizedSearchCV for systematic and random hyperparameter search. Keras, provides another deep learning library called Keras Tuner for hyperparameter search and can be integrated seamlessly with Keras models. When choosing a hyperparameter tuning tool, consider factors such as ease of use, compatibility with your existing code and libraries, scalability, and the nature of your optimization problem.

The best approach to developing a ML model involves a combination of thoughtful experimentation and systematic hyperparameter tuning. Start with a reasonable set of hyperparameters based on domain knowledge, then use tools like GridSearchCV, RandomizedSearchCV, or Hyperopt to efficiently explore the hyperparameter space. Regularly evaluate the model's performance and iteratively adjust hyperparameters based on insights gained during tuning. Additionally, consider techniques like cross-validation to ensure robustness. Ultimately, the best way depends on the specific characteristics of your dataset and problem domain.

Hyperparameter tuning is a crucial step in optimizing machine learning models for better performance. Firstly, we should define Hyperparameters. These are parameters that are not learned during training and need to be set beforehand. Examples include learning rates, regularization strengths, and the number of layers in a neural network. Furthermore, we should understand the Search Space which means , Defining the range or values that each hyperparameter can take. We must understand the possible impact of different values on the model's performance. Of course there is much we should to for this step such as choosing a search method, set evaluation criteria or using Cross validation but I want to wrap up my speech!

Overfitting occurs when a model learns to memorize the training data instead of generalizing patterns, while underfitting occurs when a model is too simple to capture the underlying structure of the data. Regularization techniques, cross-validation, and model evaluation metrics (such as accuracy, precision, recall, and F1 score) can help mitigate overfitting and underfitting issues and ensure your models generalize well to unseen data.

Overfitting occurs when a model performs well on the training data but poorly on new, unseen data. Underfitting, on the other hand, happens when a model is too simple to capture the underlying patterns in the training data. Here are strategies to mitigate overfitting and underfitting: Using more data, data augmentation, Regularization, use simpler models and feature selection are crucial when it comes to overfitting. Regarding a solution for Underfitting I can mention about Increase model Complexity , Feature Engineering , Fine-Tune Hyperparameters , Ensemble Methods , Evaluate Model Complexity , Add Interaction Terms and Experiment with different algorithms.

I would structure the approaches to combat overfitting & underfitting in 3: 1. Data-related:To reduce variance, engineer features that better capture underlying relationships in the data, thereby reducing complexity of the model in the first place & eventually dropping the redundant features 2.Model-related:Assign weights to training data points as per their importance so the model learns from informative examples rather than noisy outliers;use probabilistic techniques to estimate uncertainty in predictions & circumvent overconfidence in potentially overfit models 3. Monitoring & Evaluation:As given already, this fences hyperparameter tuning, ensemble selection, & early stopping. H2O (Python) is a specialized tool to achieve all with ease.

Overfitting occurs when the model or the algorithm fits the data too well eventually capturing all the noise in the data. It occurs when the model or algorithm shows low bias but high variance. Underfitting happens if we have less data to train our model, but quite high amount of features. This is quite simple to overcome, by using more data and also reducing the features by feature selection. Overfitting can be avoided by cross-validation, early stopping, Regularization techniques.

My personal game changers: - Regularization: Mitigates overfitting by penalizing complex models, promoting simplicity. - Diverse Training Data: Ensures robustness by exposing models to varied scenarios, reducing biases. - Hyperparameter Tuning: Fine-tuning parameters optimizes model performance, striking a balance. - Cross-Validation: Validates model robustness by testing on different subsets, minimizing bias. - Ensemble Methods: Combining multiple models reduces overfitting and enhances overall accuracy. Remember, it's the synergy of these methods that leads to a well-fitted model. Strive for balance and adaptability!

In my experience, if you have a model which is underperforming in the metrics defined, first make sure the evaluation metrics are correct for the use case. And if we are sure about the metric, then the issue might be because of data drift. That means the data distribution that the model learned while training is not the model distribution in inference time and the model is confused because of this. So check this things like Population stability Index and see if there is data drift, then try avoiding such unstable variables, and if we have that variables in a deployed model, then re-train the model in a continuous interval like every 6 months to make sure model has seen the changes.

To maintain relevance and accuracy, periodically retrain models with updated data to adapt to evolving patterns. This ensures models remain effective over time and continue to provide reliable insights. Regular updates help capture new trends and variations in the data, enhancing model performance and decision-making capabilities.

From some knowledge of Intelligent Systems Engineering I have, I can say that time to time pipeline evolution is a very important aspect of staying relevant to new data and changing demands.

The best software for underperforming your machine learning models is SKY ENGINE AI Synthetic Data Cloud platform for deep learning in vision AI. But no free training data LN!

Agreed. Over the course of time, the data undergoes drift and needs constant updates. In my experience, there are a few scenarios (especially for simpler models run on constraint hardware) where it may be better to have multiple versions of the model based on factors such as geography, time etc.

Make sure you’ve spent enough time defining what “good performance” looks like! When I am assigned to a project I often spend days interviewing people to understand what are the users’ expectations and make sure our technical performance metrics are aligned with what makes users happy :)

None of the answers tackle the real challenge one faces in real world ML. What’s “underperforming”? Overfitting” underfitting? Less recall? Less precision? Essentially before deciding your model is under performing you need to be sure about which metrics you want to focus on. High accuracy doesn’t mean high recall, low precision doesn’t mean underfitting. There’s a lot to consider, so define your problem wisely.

It bugs me that the article doesn't start from the root of the problem. Are you using the right metrics to judge your model in the first place? If not, that's the problem that first needs to be resolved. For instance, you cannot go about using metrics such as accuracy or precision for a regression problem. Maybe metrics like loss (say, RMSE) might work better in such a case. Only after such validations are you supposed to look into other causes such as malformed data, and data insufficiency, followed by experiments with the model. A successful model always relies on accurate and appropriate data and feature engineering.

Use Explainable AI to better understand how your AI model makes decisions. This tool shows you each step the model takes to reach its conclusions. With this insight, you can see if problems arise from the way the model is built or the data it uses. This understanding is key to improving your AI's performance, as it helps pinpoint where changes are needed. Simply put, Explainable AI acts like a window into your AI's thought process, making it easier to identify and fix issues.

There's no best software to improve the underperforming ML model.A thorough troubleshooting process across various aspect is crucial,with data being the cornerstone.Verify data preprocessing and feature engineering.Ensure that the correct metric is used to evualute the model.Evaluate alternative algorithms if the chosen one is not optimal.Consider regularization technique to avoid overfitting.Optimizing hyper parameters with small data can lead to overfitting, it's best to use cross validation for proper evaluation.While collecting more samples to improve the performance, maintain data quality and balance quantity with relevance.Track and monitor model performance over time for potential data drift.