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Welcome to the AI Optimization Tool

Note: This tool is designed to be used in conjunction with the AI lottery analysis feature and is not an AI prediction tool.

AI-Powered Lottery Analysis - Fantasy 5

SKAI Settings Lab

SKAI Lottery Settings Lab – Fantasy 5

Use this lab to discover the strongest training settings on real draw history, then transfer those settings into your live SKAI AI and Original AI prediction pages.

Select a Lottery:

Currently Loaded Lottery: Fantasy 5

Latest draw date 2026-01-27 00:00:00

Latest draw numbers 5, 6, 11, 32, 41

Rows in this lottery 7864

Lab runs completed 0 / 0

Estimated completion --

Settings & Training Context

Review what SKAI is currently training, then define the ranges this lab will explore to find strong settings you can reuse in your live SKAI AI and Original AI tools.

Current Analysis Parameters:
Epochs: --, Batch Size: --, Dropout Rate: --, Learning Rate: --, Activation Function: --, Hidden Layers: --, Recency Decay: --, Skip Strength: --

Parameter Configuration

Epoch Range: Enter a single value (e.g., 10) or a range (e.g., 70-100). Parameter Range 5 -1000). Batch Sizes: Enter one or more values (comma-separated) like 4, 6, 8, 12, 16, 32, 64 Dropout Rates: Enter a percentage or range (e.g., 0.1-0.5). Learning Rates: Enter one or more values (comma-separated) like 0.01, 0.001. Activation Functions: Select one or more activation functions to use (Ctrl+Click or Shift+Click to select multiple). Hidden Layers: Enter a single value or range (e.g., 1-5). Recency Decay: Enter one or more decay values (comma-separated), e.g. 0.5, 0.75, 1. Main Weights (freq, skip, hist): Enter one or more weight triplets as freq,skip,hist separated by a vertical bar, for example: 40,40,20 | 50,30,20. Values are treated as percentages and will be normalized. Skip Strength (SKIP_ALPHA): Controls how strongly skip features influence the model (higher = more weight on skip patterns). Scoring Profile: Choose how “best settings” are evaluated: more stable, more aggressive, or a balanced mix.

Step 1: choose the SKAI engine profile you want to explore (weights, blend, sampling and Top-N). Step 2: pick a Run Mode. Step 3: run the lab – it will test combinations around your choices and surface the strongest-performing profiles for this lottery.

SKAI Engine Settings (Weights & Blend)

These settings describe how SKAI weights history, skip behaviour and AI output when you later apply them on LottoExpert.net. Think of the values you enter as the center of a search region: the lab automatically explores stronger or softer variations around these settings (wider or tighter depending on the Run Mode). You may keep the suggested defaults or enter entirely custom profiles to explore different “SKAI personalities.”

Tip: Run Mode changes only the shape of the search around the values you enter: Balanced = moderate exploration, Conservative = narrow & stability-focused, Exploratory = wider & more aggressive. The lab never overwrites your entries — it only searches around them.

SKAI Window Size (draws): How many past draws SKAI considers in its main analysis window (e.g., 400–800 for Powerball-style games). Main-ball weights (Frequency / Skip / History %): Recommended bands: Balanced ≈ 30–50 / 30–50 / 10–30, Conservative ≈ 60–80 / 10–30 / 10–30, Exploratory ≈ 30–40 / 40–60 / 0–20. Values do not need to sum exactly to 100; the lab normalizes them internally. Extra-ball weights (Frequency / Skip / History %): Same idea as main weights, but for the extra ball. Typical ranges mirror the main-ball weights above. SKAI Blend (Skip % / AI %): How strongly SKAI leans on skip-driven signals versus pure AI estimates. You can enter either:
• a single value (for example 50 = 50% Skip / 50% AI), or
• a range like 10-60 to let the lab test 10%, 12%, 14%, …, 60% Skip in steps of 2. If you leave the AI % box at 0, SKAI will automatically use 100 − Skip % for each test. Balanced mode usually explores bands around 40–60% Skip; Conservative leans more to AI, Exploratory leans more to Skip. Exploration behaviour: Controls how concentrated or exploratory SKAI’s sampling is when it builds combos. Conservative mode keeps these closer to 0.5 / 0.0 / 1.0; Exploratory mode allows higher values for more varied picks. Output format (Top N): How many core numbers you intend SKAI to surface on your live tools. Common ranges: 15–25 carefully chosen numbers.

Parameter Explanations

Understanding these parameters is crucial for tailoring the model to your needs. Expand each section to learn more.

Epoch Range

The epoch range determines how many times the entire dataset is passed through the model during the training process. Training a model involves adjusting its internal parameters (weights) to learn patterns from data and make accurate predictions. Each time the model sees the dataset and updates its parameters, it completes one "epoch."

What is an epoch? An epoch represents one full pass of the training dataset through the model. During each pass:
- The model processes the data, calculates predictions, and compares them with the correct answers.
- It uses the differences (errors) to adjust its parameters, improving its ability to predict correctly on future data.

Aggregate Performance & Consistency

This simulation now tracks not only the average performance metrics (such as F1 score and average matches) but also their consistency across multiple runs (using k-fold cross-validation). In particular, we compute the standard deviation of the F1 score for each hyperparameter setting. A lower standard deviation indicates that the setting performs reliably across different subsets of data. Use this information to favor settings that not only have high average scores but are also consistent.

Performance & Best Settings

Monitor match distributions, training vs. validation loss, and the highest-scoring settings this lab discovers for you.

Matches Predicted (Live per Epoch)

We show how many draws got 0..5 matches out of the top 20 main numbers each epoch.

Training vs. Validation Loss per Epoch

Training vs. Validation Loss: The training loss measures how well the model is fitting the training data, while the validation loss shows how well the model generalizes to unseen data. Ideally, both losses decrease as training progresses. If the training loss continues to drop but the validation loss starts rising, it is a sign of overfitting. This chart provides real-time insight into the model’s learning behavior, helping you determine if and when adjustments (such as early stopping or increased regularization) are needed. Note that the moment the simulation detects any of these situations it will act automatically to make the adjustments and continue.

AI-Powered Lottery Predictions: Play Smarter, Not Harder!

Winning the lottery isn’t just about luck—it’s about numbers and strategy. With millions of possible combinations, blindly guessing is a losing approach. But what if you could narrow the field and focus on just 20 high-probability numbers instead of the entire pool? That’s exactly what our advanced AI prediction system helps you do!

Smarter Number Selection with AI

Our AI doesn’t make random guesses—it analyzes past draws to uncover hidden patterns. By identifying 20 numbers with the highest statistical relevance, AI increases the likelihood of 4 or even 5 matching numbers appearing in some draws.

What does this mean for you?
Instead of picking numbers at random or relying on personal hunches, you get AI-optimized selections that maximize your chances—while maintaining fair and realistic expectations.

Maximizing Your Wins: The AI Advantage

Why play blind when you can play smart? Our system allows you to experiment with AI settings—such as learning rates, layers, and epochs—to find the best configurations that improve accuracy.

Data-Proven Strategy

Rather than testing millions of combinations, you can strategically wheel these 20 AI-selected numbers, knowing they have a statistically better chance of including some of the winning numbers. Data shows that this method consistently outperforms random selection.

How We Measure AI Success

We use hit rate, precision, and recall to fine-tune our AI—not just generic classification metrics like F1-score. These measurements ensure that AI-selected numbers align with historical trends, providing the best possible selection guidance.

How We Fine-Tune AI for the Best Results

To deliver the most accurate predictions, we run thousands of simulations, adjusting key parameters to find the perfect balance between accuracy and efficiency:

Epochs: 5–130
Batch Sizes: 4–64
Dropout Rates: 0.2–0.5
Learning Rates: 0.002 or 0.0002
Activation Functions: Tanh, ReLU, and Sigmoid (applied across 2–4 hidden layers)

The result? A constantly evolving system that adapts to each lottery, ensuring you always get the most relevant number selections.

The Science Behind Activation Functions: ReLU vs. Tanh

AI isn’t magic—it’s a data-driven tool that learns from past lottery results. One critical part of this process is the activation function, which determines how the AI model processes information.

Why ReLU Supercharges AI for Lottery Analysis

Faster Training, Better Results

ReLU (Rectified Linear Unit) is defined as:

            f(x) = max(0, x)
        

Unlike older activation methods, ReLU keeps learning rates high, preventing slowdowns and making it easier to detect deep statistical patterns.

Real-World Benefit

Since lottery draws are based on numbers, AI needs a function that captures numerical relationships efficiently—this is why ReLU is often the top choice for complex number-based predictions.

Why Tanh is Still a Strong Option

Better for Data Centered Around Zero

Tanh (Hyperbolic Tangent) is defined as:

            f(x) = (e^x - e^(-x)) / (e^x + e^(-x))
        

Unlike ReLU, which only outputs positive values, Tanh produces outputs between -1 and 1, making it better suited for data that is naturally distributed around zero.

When to Use Tanh

For certain lotteries, where number distributions or dataset normalizations suggest a zero-centered approach is more efficient, Tanh may perform better than ReLU.

Why We Run Separate Simulations for Each Lottery

Every lottery has different rules, number ranges, and statistical behaviors, which is why AI models must be trained separately for each game.

Why You Can’t Use the Same Settings for All Lotteries

Different Number Pools: Powerball (1–69) is not the same as Lucky for Life (1–48) or Mega Millions (1–70).
Different Draw Patterns: Some lotteries favor certain number distributions over time.
Different Optimal AI Settings: The best learning rates, activation functions, and hidden layers vary per lottery.

Our Recommendation

We encourage users to run AI simulations for each lottery separately and experiment with different activation functions. Whether ReLU or Tanh performs best will depend on the specific lottery's dataset and number distributions.

Your Winning Edge: AI-Powered Predictions

Instead of guessing, leverage AI’s precision-driven number selection to improve your chances.
Use smart wheeling to focus on high-probability numbers, rather than playing blindly.
Watch AI’s predictions evolve, optimizing your number choices based on real lottery data.

Important Note: The 20 AI-selected numbers displayed are from a single run—they do not necessarily represent the best overall results. For long-term reliability, AI’s effectiveness is measured through hit rate, recall, and precision to ensure the selected numbers consistently align with historical winning patterns.

Ready to Play Smarter?

Take the guesswork out of lottery selection and let AI guide you toward higher match rates, better strategies, and more informed picks.

Introduction

The AI Settings Explorer lets you experiment with different AI settings to refine your lottery prediction analysis. Understanding how each setting works will help you maximize accuracy and improve your strategy.

Step 1: Select Your Lottery

Before adjusting any settings, you must select the lottery you want to analyze:

Open the Lottery Selection Dropdown at the top of the AI Settings Explorer.
Choose your desired lottery (e.g., Powerball, Mega Millions, EuroMillions).
Once selected, the system will load the correct dataset and settings.

Important: If you do not select a lottery, the AI will not know which dataset to analyze.

Step 2: Understanding Epochs (The Key Setting to Adjust)

An epoch represents one full pass through the lottery data during AI training. The more epochs you run, the more refined the AI’s understanding of number patterns becomes, but more is not always better.

Common Misconceptions About Epochs

"More epochs always improve accuracy." – Too many can cause overfitting (memorizing past draws instead of identifying future patterns).
"Fewer epochs mean bad predictions." – Fewer epochs can sometimes generalize patterns better and produce stronger results.

How to Experiment with Epochs

Start with the default settings we have provided.
Try increasing or decreasing the number of epochs to compare results.
Run predictions and analyze changes in accuracy.
Adjust until you find the best balance for your lottery analysis.

Step 3: Running Your Analysis

Select Your Lottery – Choose which lottery dataset to analyze.
Adjust the Epochs Setting – Start with a lower number (e.g., 10–50) and gradually increase.
Keep Other Settings Default – We have optimized them for best results.
Run the Analysis – Click the button to generate AI-powered predictions.
Compare Your Results – Observe how different epoch values affect accuracy.
Fine-Tune as Needed – If results seem too random, adjust the epochs slightly and rerun.

Common Mistakes to Avoid

Forgetting to select a lottery – The AI needs to know which dataset to analyze.
Assuming more epochs = better results – Overfitting can reduce predictive accuracy.
Changing too many settings at once – Stick to adjusting only epochs at first.
Not tracking results – Keep notes on different epoch values and how they perform.

Applying Your Best Settings

Once you have tested and found the best AI settings, you can apply them in the AI Prediction Lottery Analysis under Advanced Settings.

This ensures that your optimized AI parameters are used for precise lottery predictions.

Next Steps

Try experimenting with different epoch values and compare results.
Apply your best settings in AI Prediction Lottery Analysis under Advanced Settings.
Once comfortable, move on to the next tutorial: AI Insight Analysis.

Important Notice

This tool performs complex, resource-intensive calculations to optimize AI settings for lottery predictions. For best performance, we recommend using a desktop or laptop computer. Running this tool on a smartphone or tablet may result in prolonged computation times and performance issues.

The settings we provide by default have been extensively researched and optimized for the best results. However, this tool is available if you want to explore other settings. Using this tool is optional and not necessary to run the AI analysis. Depending on the depth of exploration, processing time can take hours or even several days if multiple settings are selected.

Note: This tool is designed to be used in conjunction with the AI lottery analysis feature and is not an AI prediction tool.

AI Optimization Tool

Understanding the Computational Load

To illustrate the massive scale of analysis being conducted in the AI-powered lottery analysis system, we break it down mathematically and visually so that users can easily understand the process. The following explanation provides factual computations showing why it takes so long to process the results.

1. Understanding the Computational Load

Each epoch represents one full pass through the historical lottery data, where the AI searches for patterns and updates its internal model.

Powerball Game Specs:

Main Pool: 69 numbers
Powerball Pool: 26 numbers
Pick Size: 5 main numbers + 1 Powerball

Each AI simulation typically involves multiple epochs, and every epoch involves millions of calculations.

2. Breakdown of the AI Training Process

At a high level, each epoch does the following:

Processes historical drawings stored in the database.
Passes each drawing through a deep learning model consisting of multiple layers.
Adjusts the model's internal weights based on how close the prediction was to actual past results.
Repeats this process for every drawing (often thousands of past draws).
Performs statistical comparisons and probability weightings for every number from 1-69 and 1-26.
Adjusts thousands or millions of tiny mathematical weights in the model.

Real Computational Example

Assume we are training for Powerball with the following parameters:

1000 past draws in the dataset
Epochs: 100 (the process is repeated 100 times)
Batch Size: 4 (4 draws are processed at a time)

Each epoch processes:
1000 draws / 4 (batch size) = 250 batches

For each batch:

Forward pass: 1,000,000+ operations (matrix multiplications)
Backward pass: Another 1,000,000+ operations (adjusting weights)

Total computations per epoch:
250 batches × 2,000,000 = 500,000,000 calculations

Running 100 epochs, the total computational load becomes:
500,000,000 × 100 = 50,000,000,000 (50 billion calculations)

That is 50 billion operations for a single training session, which explains why the analysis takes significant time.

3. How AI Learns & Refines Predictions

Each epoch refines its understanding of past winning numbers. The AI uses weight optimization algorithms to improve pattern recognition, loss function calculations to measure accuracy, and backpropagation techniques to tweak neuron weights. Imagine a giant Excel sheet where the AI multiplies and updates every cell in a massive grid billions of times per epoch. This is why the system is so powerful.

4. Visual Representation of the Scale of Computation

The following visualization illustrates the computational load per epoch in the AI-powered Powerball analysis. Each epoch performs approximately 50 billion calculations, which underscores the massive scale of processing.

Database Size	Total Computations (100 Epochs)
1000 Draws	200 Billion Calculations
5000 Draws	1 Trillion Calculations

Key Insights:

Each epoch processes all historical draws (approximately 1000 draws in this example).
Each batch involves millions of mathematical operations to adjust and refine predictions.
Total processing over 100 epochs reaches 50 billion calculations.
This illustrates why AI-based lottery prediction is a massive computational task requiring careful tuning of parameters like epochs, batch size, and learning rate.

Our AI Optimization Tool runs multiple simulations to test different settings for our prediction algorithms. It evaluates how well the AI performs under various conditions to find the best possible combination of settings.

Matches: The number of correct predictions compared to actual results.
Overall Score: A key measure of performance that evaluates multiple accuracy factors.
Precision: How often the AI’s predictions were correct out of all its predictions.
Recall: How many correct matches the AI identified out of the total possible correct matches.

The Overall Score provides a balanced evaluation of AI performance, considering accuracy, consistency, and reliability. A higher score indicates that the AI is delivering more effective predictions over multiple test runs.

Save Time: Instead of guessing the best AI settings, let this tool do the heavy lifting.
Boost Accuracy: Find the settings that maximize your chances of success.
Learn and Improve: Gain insights into how AI evaluates data and what makes predictions better.

Aim for the settings with the highest Overall Score. This ensures the AI is selecting numbers with the strongest probability of appearing in future draws.

This tool is available to everyone, and it is a great way to explore the power of AI in lottery predictions. For even deeper insights and exclusive features, consider becoming an AI Insights Member.

Term	Definition
Epochs	The number of times the AI processes the entire training dataset to learn and refine its predictions.
Batch	The number of data samples the AI processes at one time during training.
Dropout	A technique to prevent overfitting by randomly dropping a percentage of neurons during training.
LR (Learning Rate)	The step size the AI takes to adjust its predictions during training. Smaller rates are more precise but slower.
ActFn (Activation Function)	The mathematical function used to determine how the AI processes data at each neuron level.
Layers	The number of layers in the AI’s neural network, which impacts the model’s complexity and learning capability.
Actual	The actual winning numbers from the draw being analyzed.
Predicted	The numbers predicted by the AI for the draw.
Avg Matches	The average number of matches per draw across all simulations.
Avg Rank	The average ranking of predictions based on their match count and accuracy.
F1	A measure that balances precision (how often predictions are correct) and recall (how many correct numbers were captured).
5-Matches	The count of instances where all 5 predicted numbers match the actual winning numbers in a draw.
Extra Ball	The additional number (e.g., Powerball) predicted alongside the main numbers in some lottery games.
Accuracy	The percentage of predictions that were correct.
Loss	The error rate of the AI model during training; lower is better.
Prec (Precision)	The proportion of predicted numbers that are correct.
Rec (Recall)	The proportion of actual winning numbers that were predicted.
Max M (Max Matches)	The highest number of matches found in a single simulation.
Score	A composite metric that evaluates the overall performance of the prediction model.

Explore LottoExpert tools

Welcome to the AI Optimization Tool Note: This tool is designed to be used in conjunction with the AI lottery analysis feature and is not an AI prediction tool.