7. Get insights
NextBrain will provide you with several options to get insights about a machine learning model and its results, including:
The model's performance metrics: Evaluation of the model's performance on a train and test datasets. Metrics such as accuracy, precision, recall, MAE, MAPE, and R2 score can provide an indication of how well the model is performing.
Examine the confusion matrix: A confusion matrix can give you more insight into the model's performance, by showing the number of true positives, true negatives, false positives, and false negatives. It can also help you identify which types of errors the model is making.
Visualize the data and model outputs: Visualizations can help you understand the relationships between the input data and the model's predictions. For example, scatter plots can show how different features of the input data are related to the model's output.
Use feature importance techniques: Feature importance techniques, such as SHAP, can help you understand which features of the input data are most important for the model's predictions.
Use model interpretability tools: There are various interpretability tools such as the decision trees (or named as visual explainability in the dashboard menu) technique that can help you understand how the model makes its predictions.
Compare the model with alternative models: Comparing the model's performance with alternative models can provide insights into the strengths and weaknesses of the model. NextBrain will also inform you about how your model performed compared to a simpler baseline method.
You can learn more about these insights techniques on the dashboard widgets articles here: https://help.nextbrain.ai/en/category/dashboard-widgets-1tgrcb/
Overall, getting insights about a machine learning model and its results requires a combination of statistical analysis, data visualization, and model interpretability techniques. By following these steps, you can gain a better understanding of how the model works, what its limitations are, and how it can be improved.
The model's performance metrics: Evaluation of the model's performance on a train and test datasets. Metrics such as accuracy, precision, recall, MAE, MAPE, and R2 score can provide an indication of how well the model is performing.
Examine the confusion matrix: A confusion matrix can give you more insight into the model's performance, by showing the number of true positives, true negatives, false positives, and false negatives. It can also help you identify which types of errors the model is making.
Visualize the data and model outputs: Visualizations can help you understand the relationships between the input data and the model's predictions. For example, scatter plots can show how different features of the input data are related to the model's output.
Use feature importance techniques: Feature importance techniques, such as SHAP, can help you understand which features of the input data are most important for the model's predictions.
Use model interpretability tools: There are various interpretability tools such as the decision trees (or named as visual explainability in the dashboard menu) technique that can help you understand how the model makes its predictions.
Compare the model with alternative models: Comparing the model's performance with alternative models can provide insights into the strengths and weaknesses of the model. NextBrain will also inform you about how your model performed compared to a simpler baseline method.
You can learn more about these insights techniques on the dashboard widgets articles here: https://help.nextbrain.ai/en/category/dashboard-widgets-1tgrcb/
Overall, getting insights about a machine learning model and its results requires a combination of statistical analysis, data visualization, and model interpretability techniques. By following these steps, you can gain a better understanding of how the model works, what its limitations are, and how it can be improved.
Updated on: 20/02/2023
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