Machine Learning (ML) has its own vocabulary that helps in understanding the concepts, methods, and tools involved. This article provides an overview of essential ML terminology, making it easier for beginners and enthusiasts to navigate this field effectively. For more guides and insights, visit The Coding College.
Key Machine Learning Terms
- Algorithm
- A set of rules or calculations used to solve problems. In ML, algorithms are used to build models from data.
- Example: Linear Regression, Decision Trees, Neural Networks.
- Model
- The output of an ML algorithm after it has been trained on data. It is used to make predictions or decisions.
- Training Data
- The dataset used to train the ML model. It includes input features and corresponding labels (in supervised learning).
- Testing Data
- A separate dataset used to evaluate the performance of the trained model.
- Features
- Measurable properties or characteristics of the data used as input for the model.
- Example: Height, weight, and age for predicting health metrics.
- Labels
- The target variable or the output the model is trying to predict.
- Example: In a spam detection system, labels are “spam” or “not spam.”
- Supervised Learning
- A type of ML where the model learns from labeled data.
- Unsupervised Learning
- A type of ML where the model identifies patterns in unlabeled data.
- Reinforcement Learning
- A type of ML where the model learns by interacting with an environment and receiving rewards or penalties.
- Overfitting
- When a model learns the training data too well, including noise, and performs poorly on new data.
- Underfitting
- When a model is too simple and fails to capture the underlying patterns in the data.
- Hyperparameters
- Settings that are adjusted before training the model, such as learning rate or the number of layers in a neural network.
- Epochs
- The number of times the entire training dataset is passed through the model during training.
- Loss Function
- A mathematical function that quantifies the difference between the predicted and actual outputs.
- Gradient Descent
- An optimization algorithm used to minimize the loss function by adjusting the model’s parameters.
- Learning Rate
- A hyperparameter that controls how much the model’s parameters are updated during training.
- Activation Function
- A function used in neural networks to introduce non-linearity into the model.
- Example: Sigmoid, ReLU, Tanh.
- Neural Network
- A series of algorithms inspired by the human brain, used to recognize patterns and perform tasks like classification and regression.
- Backpropagation
- A method for training neural networks by propagating errors backward to update weights.
- Precision
- The ratio of correctly predicted positive observations to the total predicted positives.
- Recall
- The ratio of correctly predicted positive observations to all the actual positives.
- F1 Score
- The harmonic mean of precision and recall, providing a balanced measure of a model’s performance.
- ROC Curve
- A graphical representation of a classifier’s performance across different thresholds.
- Regularization
- Techniques used to prevent overfitting by penalizing large model coefficients.
- Example: L1, L2 regularization.
- Cross-Validation
- A technique for evaluating model performance by splitting data into training and testing subsets multiple times.
Why Understanding ML Terminology Matters
- Clear Communication
- Helps in effective communication with other ML practitioners.
- Better Learning
- Provides a strong foundation to grasp advanced concepts and techniques.
- Efficient Debugging
- A clear understanding of terms can help identify and solve issues in ML projects.
Example of Applying ML Terminology
Let’s say you are building a spam detection model:
- Algorithm: Logistic Regression.
- Features: Keywords in emails, frequency of certain words.
- Labels: Spam or not spam.
- Training Data: A dataset of emails with spam labels.
- Testing Data: A separate set of emails to evaluate the model.
- Loss Function: Cross-entropy loss to measure prediction errors.