Algorithm Model (AI Mi 8001)
This AI Algorithm achieves Impressive Win Rate compare to Random Guessing of 4D Numbers.
Core Algorithm
This algorithm uses an ensemble machine learning method that : * Builds multiple decision trees during training
* Combines predictions from all trees to reduce overfitting
Key features used for prediction : * time-based pattern analysis
* frequency: Historical occurrence count of each number
Key Technical Components
Data Preparation Pipeline : * Frequency Calculation: Tracks how often each number has historically appeared
* Digit Splitting: Separates 4D numbers into individual digits for positional analysis
Prediction Process : * Calculates probabilities using trained AI
* Selects numbers with highest predicted probabilities
Advantages of this Algorithms
Pattern Recognition : * Identifies time-based trends
* Detects “hot numbers” via frequency analysis
* Analyzes digit-position patterns (e.g. common digit combinations)
Robustness : * Handles missing/invalid data through automatic filtering
* Uses multiple validation checks for data integrity
Scalability :* Processes large amount of possible numbers
* Handle decades of historical data efficiently