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Machine learning Results Page for classification


In the dynamic world of machine learning, understanding the performance of your models is paramount. Enter OtasML, a cutting-edge visual machine-learning tool designed to empower users to train, evaluate, and refine their models with ease. At the heart of OtasML lies its Results Page, a robust platform that offers deep insights into model performance. In this article, we’ll take a detailed tour of the Results Page, exploring its features and functionalities to harness the full potential of your machine learning endeavors.

Page's configurations

Revision: Tracking the Evolution

In the realm of machine learning, iteration is key to refinement. With OtasML, each iteration of your machine learning model is termed a “revision.” These revisions capture the nuanced variations in model performance, reflecting the iterative nature of model training and evaluation.

Note: Embracing Iteration

In the ever-evolving landscape of machine learning, iteration is key to progress. With OtasML, each training session spawns a new revision, capturing the iterative journey of model development and evolution. This iterative approach ensures that users have a comprehensive view of their model's performance trajectory over time.

Chart Selection: Tailored Visualizations

The Chart section of the OtasML Results Page empowers users with the flexibility to choose from a diverse range of visual representations. Whether it's multiclass or binary classification, OtasML offers an array of charts tailored to your analytical needs. From Class-specific One-vs-Rest (OVR) Curves to Precision-Recall Curves, users can select the charts that best illuminate their model's performance landscape.

Multiclass Charts:

  1. Class-specific One-vs-Rest (OVR) Curve: Visualizes the performance of each class against all other classes.
  2. Micro-Average ROC Curve: Provides a combined ROC curve for all classes, weighted by the number of true positives for each class.
  3. Macro-Average ROC Curve: Illustrates the average performance across all classes, without considering class imbalance.
  4. Feature Importance: Displays the importance of different features in the classification process.
  5. Heatmap for Confusion Matrix (All Classes): Offers a visual representation of the confusion matrix for all classes.
  6. Main Diagonal of Confusion Matrix: Focuses on the diagonal elements of the confusion matrix, indicating correct classifications.
  7. Heatmap Confusion Matrix for a Specific Class: Zooms into the confusion matrix to highlight performance for a specific class.
  8. Precision-Recall Curve: This shows the trade-off between precision and recall for different thresholds.

Binary Charts:

  1. ROC Curve: Represents the receiver operating characteristic curve for binary classification.
  2. General Parameters: Provides an overview of general parameters used in the binary classification process.
  3. Confusion Matrix: Displays the confusion matrix specific to binary classification.
  4. Histogram: Visualizes the distribution of predicted probabilities for the binary classes.
  5. Precision-Recall Curve: Illustrates the relationship between precision and recall for binary classification.
  6. Feature Importance: Highlights the importance of features in binary classification.
Original Data: Anchoring Insights

Before diving into model evaluation, it’s essential to ground ourselves in the original data. The Original Data Table on the OtasML Results Page provides users with a snapshot of the dataset used for training and evaluation, ensuring transparency and accountability throughout the process.

General Information: Insights at a Glance

The General Information Table offers a comprehensive overview of the training process. From start time to completion time, data source name to model download links, this table serves as a centralized hub for key training session details, facilitating seamless collaboration and knowledge sharing.

Machine Learning Configurations: Fine-tuning the Model

Understanding the intricacies of model configuration is essential for optimizing performance. The Machine Learning Configurations Table on the OtasML Results Page delves into the nitty-gritty of input variables, output, and algorithm configuration, providing users with actionable insights to fine-tune their models for optimal results.

Metrics: Quantifying Performance

Metrics serve as the compass guiding our journey through model evaluation. The Metrics section of the OtasML Results Page offers a comprehensive suite of performance metrics, including precision, recall, F1-score, and support for all classes, as well as accuracy, macro average, and weighted average, empowering users to make informed decisions based on quantitative analysis.

Preview: Visualizing Insights

Rounding off the Results Page experience is the Preview section, which provides users with a visual snapshot of their model's performance. Whether it's ROC Curves, Confusion Matrices, or Feature Importance plots, the Preview section offers users a glimpse into the inner workings of their models, facilitating deeper insights and informed decision-making.


The OtasML Results Page serves as a beacon of insight into the vast expanse of machine learning. With its rich array of features and functionalities, the Results Page empowers users to unlock the full potential of their models, guiding them on a journey of discovery and refinement. So, dive in, explore, and harness the power of OtasML to unleash the true potential of your machine-learning endeavors.


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