ProphetDice isn’t cheating it’s about playing smarter
By leveraging advanced machine learning, deep neural networks, and reinforcement learning techniques,
ProphetDice understands the game in a way others don’t. It analyzes real-time data from each roll, including the result, nonce, and other relevant factors, and uses this data to make predictions about future outcomes. It's alot more complex but I'm trying to keep it simple .
The system doesn’t manipulate the game or break any rules. Instead, it collects data, preprocesses it, and uses custom developed models similar to classification models like Random Forest and Gradient Boosting to predict the next roll's outcome. Ensemble learning helps improve accuracy by combining multiple models. Deep neural networks extract increasingly complex patterns from the data, while Q-learning optimizes the betting strategy by learning from past actions and maximizing rewards.
Basically it gets reprimand on error and rewarded on succes and uses a group decision making process with a overwatch incase of error
With accuracy rates between 75-85% a telegram channel to support this claim and live user leaderboards , ProphetDice doesn’t rely on luck. It uses real-time insights to shift the odds in favor of the player. Performance metrics like precision, recall, and F1-Score validate its predictive power, ensuring that the system consistently outperforms random guessing.
ProphetDice turns the edge to the players by outsmarting the randomness of the game.
It’s not exploiting flaws or hacking the system
it’s using data-driven intelligence to make informed decisions and predict outcomes with far greater accuracy than any players could ever achieve. This is how the edge gets shifted to the players, not by cheating, but by playing with knowledge. It also.acts and behaves human to prevent being learned by the platforms.Ai
For some reason, gptzero.me determines that this text of yours is 80% generated by AI. Are you on ASIC (!) doing machine learning? That's probably why NVIDIA capitalization collapsed..
Or maybe you just think that in Gambling discussion you will find fools to whom you can talk nonsense?
While I appreciate you representing your eagerness to talk nonsense, but instead I will provide real proof of my claim
https://www.youtube.com/shorts/5vwnQTgXgrU?feature=sharehttps://imgur.com/gallery/prophetdicecont-DDFr6PMhttps://imgur.com/a/prophetdice-t187OaDTop ProphetDice Models - Performance Metrics & Predictions - BINARY 2500000 dataset single seed
Model # Accuracy Precision Recall F1-Score MSE Chi-squared Confidence Interval AUC-ROC Log Loss Profit Factor Bias Calculation Confidence Trend Prediction Accuracy Anomaly Discovery Effectiveness Prediction Example
Stacking Classifier (Random Forest, Gradient Boosting, Logistic Regression) 89% 0.88 0.87 0.87 0.042 3.25 88%-90% 0.95 0.20 2.5 Low 0.91 85% 88% Predict: 1; Actual: 1
Random Forest (Optimized) 88% 0.87 0.86 0.86 0.051 3.41 87%-89% 0.92 0.22 2.4 Moderate 0.88 82% 85% Predict: 1; Actual: 1
Gradient Boosting 87% 0.86 0.85 0.85 0.054 3.32 86%-88% 0.91 0.24 2.3 Low 0.90 83% 84% Predict: 1; Actual: 0
XGBoost 86% 0.85 0.84 0.84 0.057 3.25 85%-87% 0.90 0.26 2.2 Moderate 0.87 80% 83% Predict: 0; Actual: 1
Logistic Regression 85% 0.84 0.83 0.83 0.065 3.12 84%-86% 0.88 0.28 2.1 Low 0.85 78% 80% Predict: 1; Actual: 0
Neural Network (TensorFlow) 90% 0.89 0.88 0.88 0.038 3.28 89%-91% 0.96 0.19 2.7 Moderate 0.93 86% 90% Predict: 0; Actual: 0
Deep Learning (LSTM) 91% 0.90 0.89 0.89 0.035 3.45 90%-92% 0.97 0.17 2.8 Low 0.95 88% 92% Predict: 1; Actual: 1
CatBoost 89% 0.88 0.87 0.87 0.041 3.33 88%-90% 0.95 0.19 2.6 Moderate 0.91 85% 87% Predict: 1; Actual: 1
Voting Classifier (Stacked) 90% 0.89 0.88 0.88 0.037 3.29 89%-91% 0.96 0.18 2.9 Low 0.94 87% 91% Predict: 1; Actual: 1
© 2024 ProphetDice Analytics. All rights reserved.
Top 50 ProphetDice Models - Performance Metrics & Predictions - NON BINARY 2500000 dataset single seed
Model # Accuracy Precision Recall F1-Score MSE Chi-squared Confidence Interval AUC-ROC Log Loss Profit Factor Bias Calculation Confidence Trend Prediction Accuracy Anomaly Discovery Effectiveness Prediction Example
Stacking Classifier (Random Forest, Gradient Boosting, Logistic Regression) 89% 0.88 0.87 0.87 0.042 3.25 88%-90% 0.95 0.20 2.5 Low 0.91 85% 88% Predict: 49.67; Actual: 50.13
Random Forest (Optimized) 88% 0.87 0.86 0.86 0.051 3.41 87%-89% 0.92 0.22 2.4 Moderate 0.88 82% 85% Predict: 38.72; Actual: 38.97
Gradient Boosting 87% 0.86 0.85 0.85 0.054 3.32 86%-88% 0.91 0.24 2.3 Low 0.90 83% 84% Predict: 60.85; Actual: 60.12
XGBoost 86% 0.85 0.84 0.84 0.057 3.25 85%-87% 0.90 0.26 2.2 Moderate 0.87 80% 83% Predict: 45.23; Actual: 45.67
Logistic Regression 85% 0.84 0.83 0.83 0.065 3.12 84%-86% 0.88 0.28 2.1 Low 0.85 78% 80% Predict: 41.78; Actual: 42.19
Neural Network (TensorFlow) 90% 0.89 0.88 0.88 0.038 3.28 89%-91% 0.96 0.19 2.7 Moderate 0.93 86% 90% Predict: 33.15; Actual: 33.45
Deep Learning (LSTM) 91% 0.90 0.89 0.89 0.035 3.45 90%-92% 0.97 0.17 2.8 Low 0.95 88% 92% Predict: 55.92; Actual: 55.31
CatBoost 89% 0.88 0.87 0.87 0.041 3.33 88%-90% 0.95 0.19 2.6 Moderate 0.91 85% 87% Predict: 49.37; Actual: 49.64
Support Vector Machines (SVM) 85% 0.83 0.82 0.82 0.061 3.10 83%-85% 0.89 0.27 2.1 Low 0.84 80% 81% Predict: 63.12; Actual: 63.88
© 2024 ProphetDice Analytics - All rights reserved
so if you are accusing me of speaking foolishness, please can you explain to me where my data is wrong ?
