MF3Classifier

Model Overview

This is a machine learning pipeline designed to predict mutual fund performance using both numerical and categorical features. The model combines preprocessing steps with a Random Forest classifier, making it suitable for financial data analysis.

Model Architecture

The model uses a two-branch preprocessing pipeline followed by a Random Forest classifier:

Preprocessing Pipeline

1. Numerical Features Branch

   • Features: ['AUM']
   • Transformation: StandardScaler

2. Categorical Features Branch

   • Features: ['AMC', 'Fund Category', 'Sub-Sheme', 'Investment Type', 'Growth Option']
   • Transformations:
     • OneHotEncoder (non-sparse output, handles unknown categories)
     • Feature Selection (SelectKBest with mutual_info_classif, k=30)

Classifier

• Model: RandomForestClassifier
• Key Parameters:
  • n_estimators: 30
  • max_depth: 20
  • min_samples_split: 10
  • min_samples_leaf: 5
  • n_jobs: -1 (parallel processing)
  • random_state: 42

Use Cases

• Mutual fund performance prediction
• Investment strategy optimization
• Portfolio management
• Risk assessment

Model Parameters

Preprocessing Configuration

• Numerical Features:

  • StandardScaler with default parameters
  • Handles mean centering and scaling

• Categorical Features:

  • OneHotEncoder:
    • handle_unknown: 'ignore'
    • sparse_output: False
    • dtype: numpy.float64
  • Feature Selection:
    • Method: SelectKBest with mutual_info_classif
    • Number of features: 30

Random Forest Configuration

• Tree Structure:

  • Maximum depth: 20
  • Minimum samples for split: 10
  • Minimum samples per leaf: 5

• Ensemble Settings:

  • Number of trees: 30
  • Feature selection: sqrt (auto)
  • Bootstrap: True
  • Criterion: gini

Technical Details

File Information

• Model Type: Scikit-learn Pipeline
• Last Updated: November 3, 2024

Input Features

1. Numerical Features:

   • AUM (Assets Under Management)

2. Categorical Features:

   • AMC (Asset Management Company)
   • Fund Category
   • Sub-Scheme
   • Investment Type
   • Growth Option

Limitations and Considerations

• The model uses mutual_info_classif for feature selection, which may not capture all relevant relationships
• Feature selection is limited to top 30 features
• Performance may vary with unknown categories due to the 'ignore' setting in OneHotEncoder

Usage Notes

• The model supports parallel processing (n_jobs=-1)
• Handles unknown categories in categorical features gracefully
• Uses standard scaling for numerical features
• Designed for production use with joblib serialization

Download Modal

To download the pre-trained MF3Classifier model, use the link below:

Download MF3Classifier Model