Simply ML

Models Coming Soon

We're constantly working to expand Simply ML's capabilities. The following machine learning models are currently in development and will be available in future releases:

Random Forest

Type: Ensemble method for classification and regression

Random Forest is a powerful ensemble learning method that builds multiple decision trees and combines their predictions. It reduces overfitting compared to single decision trees and provides excellent accuracy across a wide range of problems.

Why Random Forest?

  • High Accuracy: Often achieves excellent results with minimal tuning
  • Handles Non-Linearity: Captures complex patterns naturally
  • Feature Importance: Automatically identifies which features matter most
  • Robust to Outliers: Less sensitive to noisy data than many algorithms
  • No Feature Scaling Needed: Works directly with raw data
  • Handles Mixed Data: Can process both numerical and categorical features

Perfect For:

  • When you need high accuracy with minimal preprocessing
  • Understanding which features drive predictions
  • Both classification and regression tasks
  • Handling datasets with many features
  • Getting reliable predictions across diverse problems

Decision Trees

Type: Tree-based method for classification and regression

Decision Trees create a flowchart-like structure where each internal node represents a decision based on a feature, each branch represents the outcome of that decision, and each leaf node represents a prediction. They're highly interpretable and easy to visualize.

Why Decision Trees?

  • Easy to Understand: Visual tree structure shows decision logic
  • No Preprocessing Required: No need to standardize or normalize
  • Handles Non-Linear Relationships: Captures complex patterns
  • Mixed Data Types: Works with numerical and categorical features
  • Feature Interactions: Automatically discovers feature combinations
  • Fast Training: Quick to build even on large datasets

Perfect For:

  • When interpretability and explainability are crucial
  • Presenting decision logic to non-technical stakeholders
  • Quick exploratory analysis and prototyping
  • Understanding feature interactions
  • Baseline models before trying more complex algorithms

What's Next?

These models are actively being developed and tested. We're focused on ensuring they integrate seamlessly with Simply ML's intuitive interface while maintaining the same ease of use you've come to expect.

Coming in Future Updates:

  • ✨ Random Forest Classification - Ensemble classifier with high accuracy
  • ✨ Random Forest Regression - Ensemble regressor for continuous predictions
  • ✨ Decision Tree Classification - Interpretable tree-based classifier
  • ✨ Decision Tree Regression - Interpretable tree-based regressor

Stay Updated

Want to be notified when these new models are released? We'll announce all new features through:

  • Email Updates: Subscribe at info@simplyml.com
  • Release Notes: Check the application for update notifications
  • Social Media: Follow us on Twitter, GitHub, and LinkedIn

💡 Have Suggestions?

We value your feedback! If you have suggestions for other models you'd like to see in Simply ML, or feature requests for the upcoming models, please reach out:

  • Email: support@simplyml.com
  • Subject Line: "Feature Request: [Your Suggestion]"

Your input helps us prioritize development and ensure Simply ML meets your machine learning needs.

In the Meantime

While we work on these exciting new models, Simply ML already offers 12 powerful machine learning algorithms covering a wide range of use cases:

  • Regression: Simple, Multiple, Polynomial, Lasso, Ridge, Elastic Net, KNN, SVR
  • Classification: Logistic, Ridge Logistic, KNN, SVM

Explore the sidebar to learn about each model and discover which one best fits your data analysis needs!