Your Machine Learning model is underperforming. How can you improve its accuracy?

To make your machine learning better: 1.Try new ways to describe the data (feature engineering). 2.Test different methods to see which one works best (algorithm selection). 3.Adjust the settings of the method you choose to make it work even better (hyperparameter tuning). 4.Combine guesses from different models to get a better overall result (ensemble methods). 5.Use tricks to make your training data bigger and more varied (data augmentation). 6.Make sure your model doesn't just memorize the training data (regularization). 7.Look at where your model makes mistakes to figure out how to improve it (error analysis). 8.Understand why your model makes certain decisions to refine it even further (model interpretation).

To make your Machine Learning model perform better, the key is to improve your data. Begin by carefully examining your dataset for errors, such as missing information or strange values. If needed, include more data to give your model a clearer picture of the problem you're trying to solve. After that, tidy up your data by organizing it neatly and removing any unusual bits. Keep in mind that having good-quality data is crucial for increasing the accuracy of your model and getting better results.

To make your machine learning better: 1.Create additional features (feature engineering). 2.Test different methods to see which one works best (algorithm selection). 3.Apply hyperparameter tuning using methods like Bayesian optimization/grid search. 4.Combine prediction from different models to get a better overall result (ensemble methods). 5.Make your training data bigger(data augmentation). 6.Make sure your model doesn't just memorize training data (regularization). 7.Do error analysis and apply transformations and repeat 1-6. 8.Get a summary of models using XAI. 9.There could be data drift/concept drift so retrain model on new data or try Online Machine learning instead of offline machine learning using RiverML/vowpal wabbit.

To enhance an underperforming ML model, study the data quality, identify anomalies, and ensure balance in class distribution. Next, fine-tune hyperparameters through techniques like grid search or Bayesian optimization. Experiment with alternative algorithms to gauge performance improvements. Analyze results, including metrics like precision, recall, and F1-score. Iterate, adjust features, preprocessing steps, or incorporate ensemble methods. If on AWS; use Sage Maker for model training, tuning, and deployment, Ground Truth for labeling high-quality training data, and Glue for data preprocessing, to aid this process

We can improve model accuracy by: * Preprocessing of data * Exploring different encoding methods for categorical variables * Better Understanding of data through data Visualisation * Normalisation and Standardisation of data * We have to select best features which will show effect on target variable * Exploring different feature engineering techniques(Aggregation, new features) * Exploring different algorithms * Improvisation of parameters through Hyperparamter tuning (Random Search, Grid Search CV) * Different Cross Validation Techniques( K Fold, Stratified K fold, Leave one out and Leave P out)

While there are multiple ways to improve a model's accuracy, a key strategy is hyperparameter tuning. Unlike model parameters automatically learned during training, hyperparameters are pre-defined settings. These settings, like the learning rate in gradient descent or the number of hidden layers in a neural network, can significantly impact performance. Hyperparameter tuning involves efficiently exploring different combinations of values for these settings, often through automated techniques like grid search or random search. By evaluating the model's performance on a validation set with each combination, you can identify the set that yields the best results, ultimately fine-tuning your model and squeezing out additional accuracy.

It is important to understand the difference between parameters and hyperparameters. Hyperparameters are set before training the model either manually based on some trial and error or using some hyperparameter tuning method like grid search, random search etc. They help to optimize the performance of the model by influencing the learning process. Some examples of hyperparameters are max_depth in decision trees, learning rate in gradient descent etc. Parameters are not set manually like hyperparameters. They are learned by the model from the data during training process. Their value keep changing with each iteration until some stopping criteria is applied. The most common example of parameters is coefficients in linear regression models.

Every model has its unique set of hyperparameters. Identify the crucial ones and understand their influence on the model's learning process. Tuning Techniques: ?Grid Search: Explores all possible hyperparameters combinations within range, ensuring thoroughness, but computationally expensive ?Random Search: Samples hyperparameters values randomly, being faster but potentially missing optimal combinations ?Bayesian Optimisation: Learns from past attempts and performance evaluations, and finds optimal combinations efficiently (requires expertise) If model understanding is crucial, consider simpler models with fewer hyperparameters. Start with basic tuning, then refine based on results. Remember, hyperparameter tuning is an iterative process.

Hyperparameter tuning is a crucial method for machine learning model optimisation. Different parameters of learning algorithms control regularisation, learning rates, complexity, and other aspects. Performance may be hampered by default settings. To determine the ideal values for a particular dataset and use case, tuning investigates parameter configurations. Setting reasonable parameter ranges, selecting effective tuning techniques like grid/random search or Bayesian optimisation, and using cross-validation to assess the outcomes. Overfitting and underfitting are balanced via parameter tuning.

In my experience, hyperparameter tuning should align with the nature of the data and the specific task at hand. This becomes especially crucial when working with deep learning models, as their iterative learning process, epochs, involves multiple passes over the data.

Experimenting with different ML models can be helpful to choose the most suitable model meeting your results requirements and other constraints like computational cost etc. But instead of blindly selecting a couple of ML models to experiment, you should know the use cases, strengths and weaknesses of each of these models. For example, Linear Regression can't be used to solve a classification problem. Logistic Regression should be chosen in that case. This is just the simplest example. There are other factors also which should be considered while making model selection.

I think it is key to consider a wide portfolio of models when you are looking for the best performance. Whether in the initial experimentation stage or in the model monitoring stage, it is a good practice to have a wide range of possibilities. This way, you can consider different options whenever necessary.

Choose the different model types for different set of requirements. Choose Linear models for smaller datasets and linear relationships Decision trees or random forest is good for handling complex features and non-linear relationships SVM is powerful for classification tasks,especially with high-dimensional data. Neural networks can be very flexible and powerful, but require more data and careful tuning Use metrics relevant to your task (accuracy, precision, recall, etc.) to compare different models on a separate validation set. Combining multiple models (e.g., through bagging or boosting) can often improve performance beyond any single model

Model Interpretability: Enhance model interpretability to gain insights into its decision-making process and identify potential sources of error. Techniques like SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis can provide valuable insights into model behavior and guide improvement efforts

Exploring various models is key to identifying the most suitable one for your specific problem. Different models have unique strengths and are suited to different types of data and problem structures. For instance, while linear models may suffice for problems with linear relationships, complex problems with nonlinear patterns might benefit from tree-based models or neural networks. Experimentation with a range of models, from simple to complex, can provide insights into which model best captures the patterns in your data.

Evaluating your model's accuracy thoroughly involves understanding where and why errors occur. Here’s how to refine this process: Use a Confusion Matrix: Assess true vs. predicted labels to calculate key metrics like accuracy, precision, recall, and F1-score. Analyze Learning Curves: Compare training vs validation errors to identify overfitting or underfitting. Conduct Error Analysis: Examine misclassified examples to discover patterns or causes of errors. Cross-Validation: Implement CV to ensure your model's performance is consistent across different subsets of data. Utilize Performance Metrics: Beyond basic metrics, explore others like AUC-ROC for classification problems or MAE/MSE for regression to get an insight into model performance.

Be careful in choosing the evaluation metrics for your model's performance evaluation. There are separate evaluation metrics based on the type of problem you are solving. For regression problems, RMSE, R-squared etc. are commonly used. For classification problems, confusion matrix, accuracy, precision, recall, f1-score etc. are used. In classification problems, if the dataset is highly imbalanced, accuracy can be a misleading metric. In that case, you should prefer other metrics like precision, recall, f1-score etc. based on the problem. It depends on what type of error (Type I or Type II) is more critical for your problem.

Apart of the common performance metrics, I recommend to use XAI techniques to understand what are the reasons for prediction fails and the way to reverse them. Local explanations using SHAP in fact is a great option to do that.

You may also try 1. Hyperparameters tunning 2. Feature engineering 3. Train and test the model using different algorithms and see which one will perform better depending on the complexity of the dataset

Careful evaluation of your model's performance is crucial in understanding and improving its accuracy. Employ a variety of metrics and diagnostic tools like confusion matrices, learning curves, and error analysis to gain a comprehensive view of your model's strengths and weaknesses. For instance, a learning curve can reveal whether additional data might improve the model or if the model is suffering from high bias or variance, guiding your next steps in model improvement.

From experience with Databricks ... if your ML model is not meeting expectations, experiment and iterate to make improvements: Hyperparameter optimization: use tools like Databricks MLflow Hyperopt to automatically search for best hyperparameters. Experiment with different configurations to find the optimal settings for your model. Feature engineering: Improve the quality of input data by creating new features or transforming existing ones. Use MLflow tracking to monitor the performance of different feature engineering techniques. Model selection: Try out different algorithms and model architectures to determine which model is best suited for your specific task. Compare the performance of models using MLflow's model tracking features.

MLflow is a good option to track experiments with different configurations of parameters 9r using different options for training data. It allows building portfolio and coming back to previous versions of you need it.

Great article, well explained points, To improve machine learning model's accuracy sometimes you could find that by tuning and and using different different models to check which is good fit for your problem. And that's true machine learning is iterative process, as lifecycle of machine learning model's increases models efficiency also get increased.

Machine learning model development is inherently iterative. Continuously experiment with different combinations of data preprocessing, model architectures, and evaluation strategies. Utilize tools like TensorBoard or MLflow to track and visualize your experiments. Incorporate feedback from various stakeholders to refine your approach. This iterative process is key to evolving and enhancing your model, adapting to new data, and responding to changing requirements.

Add more data, treat missing and outlier values, select right features, do feature engineering, tune your algorithm hyperparameters, Use ensemble methods and Cross Validation

Many data scientists make the mistake of focusing on the models and not on the data. We've moved from a model-focused discipline to a data-focused one. If your model isn't performing as expected, first question the data you're using to train it.

The two most common approaches are: 1. Fine-tuning hyperparameters in search of an ideal combination for your problem. 2. Testing new models and evaluating their performances compared to your baseline. However, once you have evaluated these two points and assuming that the amount of data you have is already at its limit, you can train a generative model (or use a pre-trained model that fits your case) to generate new synthetic data that can be used for your task, thereby expanding your data set. For instance, you can use a Variational Auto Encoder to generate new instances that were not present in your original data set. This can enhance your model's performance as it will have more instances to train on and assess performance.

Consider XAI as an alternative to understand predictions. Using SHAP or LIME you can complement model performance with interpretability. Take a look of that!

Performance depends on what you measure - this does not always have to be model accuracy. The actual application of your model may help guide your decision in what to optimize for. Simple example from boolean classification: If you want to predict impending traffic incidents in a car with an automatic emergency breaking system, you want to maximise the degree to which you can trust any single ?accident“ predicion of your model. The heavy impact of a false positive suggests a focus on precision as a performance metric. If instead your system does not trigger an emergency braking but simply flashes a small red light saying ?be careful, elevated risk for accidents“, you may rather want to focus on recall to avoid false negatives.

Investigate drift in input feature distributions. If features are changing your model needs to be updated. Otherwise you should expect worsen model performance.