The Basics of Machine Learning
Last updated
Last updated
Welcome to Sportstensor, part of the Bittensor subnet! This guide will introduce you to the basics of machine learning (ML), targeted specifically for beginners and new miners in our community.
Machine Learning (ML) is a subset of artificial intelligence (AI) focused on building systems that learn from data to improve their performance over time without being explicitly programmed. ML is widely used in various fields, including sports analytics, to make data-driven decisions and predictions. It is important to note that the machine learning process is an iterative process, aimed at developing the best possible model, tweaking different things at different steps of the process.
1. Data
Training Data: This is the dataset used to train a machine learning model. It includes input-output pairs where the output is known. The model uses this data to learn patterns and relationships.
Validation Data: This is a subset of the training data used to tune model parameters and make decisions about model configuration. It helps in selecting the best model and avoiding overfitting (this occurs when the model learns “too perfectly” the training data, and it is not generic enough to successfully predict on unseen data.
Testing Data: This is a separate dataset used to evaluate the performance of the trained model. It helps in assessing how well the model generalizes to new, unseen data.
2. Models
Algorithms: These are mathematical procedures or formulas for solving problems. Common algorithms include:
Regression models: Used for predicting continuous outcomes, or in case of logistic regression binary outputs, such as true/false
Decision Trees: Used for classification and regression tasks.
Neural Networks: Used for complex tasks such as image and speech recognition.
Parameters: These are the elements of the model that are learned from the training data, such as the weights in a neural network.
3. Training
The process of feeding data into an algorithm to help it learn the patterns and relationships within the data. This involves adjusting the model parameters to minimize the difference between predicted and actual outcomes.
4. Evaluation
Measuring the performance of a model using metrics such as:
Accuracy: The percentage of correctly predicted instances.
Precision: The proportion of true positive predictions among all positive predictions.
Recall: The proportion of true positive predictions among all actual positives.
F1 Score: The harmonic mean of precision and recall, providing a balance between the two.
5. Deployment
Integrating the model into a real-world application where it can make predictions on new data.
1. Data Collection
Gather data relevant to the problem you want to solve. For instance, in sports analytics, this could include player statistics, game results, and other performance metrics. Data can be collected from various sources like sensors, databases, and APIs.
2. Data Preprocessing
Clean and preprocess the data to ensure it is suitable for training. Here a short list of some steps it could involve:
Handling Missing Values: Filling in or removing missing data points.
Normalizing Data: Scaling features to a standard range. (some models, such as neural network, work better with data that is normalized, for example with values between 0 and 1)
Splitting Data: Dividing the data into training, validation, and testing sets. A common practice is to use 70% of the data for training, 15% for validation, and 15% for testing.
Selecting an appropriate machine learning algorithm depends on the problem you're trying to solve. Here are a few common types:
Regression: Used for predicting continuous outcomes, such as sales figures.
Classification: Used for predicting categorical outcomes, such as spam detection.
Decision Trees: Useful for both regression and classification tasks. They work by splitting the data into subsets based on the value of input features, making them easy to interpret and understand.
Neural Networks: Can be used for complicated tasks such as image recognition, speech recognition, and natural language processing. They are highly effective but require a large amount of data to perform well. Neural networks consist of layers of interconnected nodes, or neurons, which can learn to represent and recognize intricate patterns in data.
4. Training the Model
Use the training data to train your model. This involves feeding the data into the algorithm and adjusting the model parameters to learn the patterns. The training process might include techniques like cross-validation to ensure the model generalizes well.
5. Evaluating the Model
Test the model on the validation data to tune the model parameters and select the best model configuration. After tuning, evaluate the final model on the testing data to assess its performance. Common evaluation metrics include accuracy, precision, recall, and F1 score. Based on the evaluation results, you may need to adjust the model to improve its performance.
6. Improving the Model
Fine-tune the model parameters and experiment with different algorithms to improve performance. Techniques such as hyperparameter tuning, feature selection, and ensemble methods can be used to enhance the model.
7. Deploying the Model
Implement the model into your application or system to start making predictions on new, unseen data.
