Machine learning model [Neurosurgery Wiki]

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====== Machine learning model ======

A [[machine learning]] [[model]] is a mathematical representation or algorithmic structure that is trained on [[data]] to make [[prediction]]s, [[decision]]s, or [[classification]]s without being explicitly programmed. Machine learning models are designed to learn [[pattern]]s and [[relationship]]s within the data and generalize from that knowledge to make predictions or take actions when presented with new, unseen data. These models are a fundamental component of machine learning and [[artificial intelligence]] systems. Here are the key aspects and types of machine learning models:

[[Input Data]]

[[Training Data]]

[[Algorithm]]: The choice of [[machine learning algorithm]] determines the type of model you are building. Common types of machine learning algorithms include:

[[Supervised Learning]]: Algorithms learn from labeled data and make predictions or classifications. Examples include linear regression, logistic regression, decision trees, and neural networks.

[[Unsupervised Learning]]: Algorithms uncover patterns in data without labeled targets. Examples include clustering algorithms like k-means and dimensionality reduction techniques like principal component analysis (PCA).

Reinforcement Learning: Algorithms learn through interaction with an environment to maximize rewards. They are commonly used in autonomous systems and game-playing.

Semi-Supervised Learning: A combination of supervised and unsupervised learning that leverages both labeled and unlabeled data.
Deep Learning: Neural networks with multiple layers, suitable for tasks such as image recognition, natural language processing, and speech recognition.

[[Feature Engineering]]: Feature engineering involves selecting, transforming, or creating relevant features from the raw data to improve the model's performance. Effective feature engineering can significantly impact a model's accuracy.

[[Model Training:]] During the training process, the algorithm adjusts the model's parameters based on the training data to find the optimal representation that minimizes a chosen loss function. Training typically involves an iterative optimization process.

Hyperparameter Tuning: Machine learning models often have hyperparameters, which are settings that control the learning process. Hyperparameter tuning involves selecting the best hyperparameters to improve the model's performance.

Model Evaluation: The model's performance is assessed using evaluation metrics on a separate dataset called the validation or test set. Common evaluation metrics depend on the problem and may include accuracy, precision, recall, F1-score, mean squared error and more.

[[Model Deployment]]: Once the model is trained and evaluated, it can be deployed in real-world applications to make predictions on new, unlabeled data. Deployment may involve integrating the model into software, websites, or other systems.

[[Monitoring]] and [[Maintenance]]: [[Machine learning model]]s may require ongoing monitoring and maintenance to ensure they continue to perform well as data distributions change over time. Re-training and updating models may be necessary.

Machine learning models have a wide range of applications across various domains, including natural language processing, computer vision, healthcare, finance, recommendation systems, autonomous vehicles, and many more. The choice of model and algorithm depends on the specific problem, data, and objectives of the machine learning project.

===== Machine learning in neurosurgery =====
[[Machine learning in neurosurgery]].