Machine Learning Terms Glossary: Machine Learning Terms in 2024
A
Accuracy
Accuracy is a performance metric that measures the overall correctness of predictions made by a machine learning model.
Activation Function
An activation function is a mathematical function applied to the output of a neuron in an artificial neural network.
Algorithm
An algorithm is a step-by-step procedure or set of rules designed to solve a specific problem or execute a specific task.
Anomaly Detection
Anomaly Detection is the task of identifying instances that deviate significantly from the normal behavior or pattern of a dataset.
Area Under The Curve (Auc)
The Area Under the Curve (AUC) is a metric used to evaluate the performance of a classification model by measuring the area under the ROC curve.
Artificial Neural Network
An artificial neural network (ANN) is a computational model inspired by the structure and function of biological neural networks.
Auc-Roc
The AUC-ROC is the area under the ROC curve, providing an aggregate measure of a classification model's performance.
Autoencoder
An Autoencoder is a type of neural network architecture used for unsupervised learning and dimensionality reduction.
Automl
AutoML, or Automated Machine Learning, refers to the use of AI algorithms to automatically build and optimize machine learning models without human intervention.
B
Backpropagation
Backpropagation is a method used to train neural networks by computing the gradient of the loss function with respect to the network's weights.
Bagging
Bagging is an ensemble learning technique that involves training multiple models on different random subsets of the training data and averaging their predictions.
Batch Gradient Descent
Batch Gradient Descent is a type of gradient descent algorithm that updates model parameters using the entire training dataset at once.
Batch Normalization
Batch normalization is a technique used to normalize the inputs of each layer in a neural network to improve training performance and stability.
Batch Size
Batch Size is a hyperparameter that determines the number of training samples used in each iteration of the optimization algorithm during model training.
Bias
In machine learning, bias refers to the tendency of a machine learning algorithm to consistently learn the same wrong thing.
Bias Correction
Bias Correction is a technique used to correct for bias in models by applying adjustments or corrections.
Bias-Variance Tradeoff
The bias-variance tradeoff is the balance between a model's ability to make accurate predictions on new, unseen data (bias) and its sensitivity to fluctuations in the training data (variance).
Big Data
Big data refers to extremely large and complex datasets that cannot be easily managed, processed, or analyzed using traditional data processing techniques.
Boosting
Boosting is an ensemble learning technique that trains models sequentially, with each model placing more importance on the misclassified instances from the previous models.
Bootstrapping
Bootstrapping is a resampling technique where multiple training datasets are created by sampling with replacement from the original dataset.
C
Classification
Classification is a supervised learning task that involves assigning a label or category to input data based on its features.
Cloud Computing
Cloud Computing refers to the delivery of computing services, such as storage, processing power, or AI algorithms, over the internet.
Clustering
Clustering is an unsupervised learning task that involves grouping similar data points together based on their characteristics or features.
Confusion Matrix
A confusion matrix is a table used to describe the performance of a classification model by comparing its predicted labels with the true labels of the test data.
Convolutional Neural Network
A convolutional neural network (CNN) is a type of artificial neural network that is particularly effective for analyzing visual data.
Convolutional Neural Network (Cnn)
A Convolutional Neural Network (CNN) is a type of neural network commonly used for image recognition and processing.
Cost Function
A Cost Function is a measure of the error or difference between the predicted output of a Machine Learning model and the actual output.
Cross-Validation
Cross-validation is a technique used to assess the performance of a machine learning model by splitting the available data into multiple subsets for training and evaluation.
D
Data Augmentation
Data augmentation is a technique used to artificially increase the size of a training dataset by creating modified versions of existing data.
Data Preprocessing
Data preprocessing is a crucial step in machine learning that involves cleaning, transforming, and preparing the data for training and testing.
Decision Tree
A decision tree is a flowchart-like structure that is used to model and analyze decisions or decision-making processes.
Deep Learning
Deep learning is a subset of machine learning that focuses on artificial neural networks with multiple layers, allowing for complex and hierarchical representations of data.
Deep Reinforcement Learning
Deep Reinforcement Learning is a combination of deep learning and reinforcement learning, where neural networks are used to learn complex policies.
Deployment
Deployment refers to the process of putting a trained machine learning model into production and making it available for use.
Dimensionality Reduction
Dimensionality Reduction is a technique used to reduce the number of input features while retaining important information.
Distributed Computing
Distributed Computing refers to the use of multiple computers or servers to solve complex problems or process large amounts of data.
Dropout
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly disabling a proportion of neurons during training.
E
Early Stopping
Early Stopping is a technique used to prevent overfitting by monitoring the performance of a Machine Learning model on a validation set and stopping the training when the performance starts to degrade.
Ensemble Learning
Ensemble learning is a machine learning technique that combines multiple models or classifiers to improve the overall predictive performance.
Epoch
In machine learning, an epoch refers to one complete pass of the entire training dataset through the model during the training process.
Exploratory Data Analysis
Exploratory data analysis (EDA) is an approach to analyze and summarize the main characteristics of a dataset to gain insights and make informed decisions.
F
F1 Score
The F1 score is a metric used to measure the tradeoff between precision and recall in classification tasks, calculated as the harmonic mean of precision and recall.
Feature
A Feature is an individual measurable property or characteristic of a phenomenon being observed.
Feature Engineering
Feature Engineering is the process of transforming raw data into a format that can be used by machine learning algorithms, often involving creating new features.
Feature Extraction
Feature extraction is the process of selecting and transforming relevant features from the raw data to be used as input for machine learning algorithms.
Feature Importance
Feature Importance is a measure of the relevance or contribution of an input feature in a machine learning model's predictions.
Feature Scaling
Feature Scaling is the process of normalizing or standardizing the range and distribution of input features, ensuring that each feature contributes equally to the machine learning model.
Feature Selection
Feature Selection is the process of selecting a subset of relevant features from the original feature set.
G
Gaussian Distribution
The Gaussian distribution, also known as the normal distribution or bell curve, is a common continuous probability distribution used in statistics and machine learning.
Generative Adversarial Network (Gan)
A Generative Adversarial Network (GAN) is a type of neural network architecture used for generative modeling.
Generative Adversarial Networks
Generative Adversarial Networks (GANs) are a type of neural network architecture that consists of two components: a generator network and a discriminator network, trained together to generate realistic data.
Gradient Boosting
Gradient Boosting is an ensemble learning technique that combines multiple weak models into a strong model, with each model sequentially learning from the mistakes of the previous models.
Gradient Descent
Gradient descent is an optimization algorithm used to minimize the loss function and find the optimal values of a model's parameters.
Grid Search
Grid Search is a technique used to find the best combination of hyperparameter values for a Machine Learning model by exhaustively searching through a specified subset of the hyperparameter space.
H
Hyperparameter
A hyperparameter is a parameter that is not learned directly from the training data but is set manually before training a machine learning model.
Hypothesis Testing
Hypothesis testing is a statistical method used to make inferences and draw conclusions about a population based on a sample of data.
I
Imputation
Imputation is the process of filling in missing data values with estimated values based on the available information in the dataset.
Inference
In machine learning, inference refers to the process of using a trained model to make predictions or decisions on new, unseen data.
K
K-Means Clustering
K-means clustering is a popular unsupervised learning algorithm used to partition a dataset into K non-overlapping clusters based on the similarity of the data points.
K-Nearest Neighbors
K-nearest Neighbors (KNN) is a non-parametric classification algorithm that makes predictions based on the majority vote of the K nearest training examples in the feature space.
Kernel Function
A Kernel Function is a mathematical function that transforms input data into a higher-dimensional space, allowing non-linear classification in SVMs.
L
L1 Regularization
L1 regularization, also known as Lasso regularization, is a technique used to add a penalty term to the loss function to encourage sparsity in the model's weights.
L1 Regularization (Lasso)
L1 Regularization, also known as Lasso, is a regularization technique that adds the sum of the absolute values of the coefficients to the loss function.
L2 Regularization
L2 regularization, also known as Ridge regularization, is a technique used to add a penalty term to the loss function to prevent large weight values.
L2 Regularization (Ridge)
L2 Regularization, also known as Ridge, is a regularization technique that adds the sum of the squared values of the coefficients to the loss function.
Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA) is a probabilistic generative model used for topic modeling.
Learning Rate
The learning rate is a hyperparameter that controls the step size or rate at which a machine learning model learns from the training data.
Logistic Regression
Logistic regression is a statistical model used to predict the probability of a binary or categorical outcome based on one or more independent variables.
Long Short-Term Memory
Long Short-Term Memory (LSTM) is a type of recurrent neural network that addresses the vanishing gradient problem and can retain information for long periods.
Loss Function
A loss function is a measure of how well a machine learning model is able to predict the correct outcome for a given input.
M
Machine Learning
Machine Learning is a field of Artificial Intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to learn and make decisions or predictions without explicit programming.
Min-Max Scaling
Min-max scaling is a technique used to normalize the features of a dataset to a fixed range, typically between 0 and 1.
Mini-Batch Gradient Descent
Mini-batch Gradient Descent is a type of gradient descent algorithm that updates model parameters using small random batches of the training data.
Model Evaluation
Model Evaluation is the process of assessing the performance of a trained machine learning model on unseen data.
N
Natural Language Processing
Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and human language, enabling computers to understand, interpret, and generate human language.
Natural Language Processing (Nlp)
Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and human language.
Neural Network
A neural network is a computational model inspired by the structure and function of biological neural networks.
Nlp
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language.
O
One-Hot Encoding
One-hot encoding is a technique used to represent categorical variables as binary vectors or multiple binary variables.
Optimization Algorithm
An Optimization Algorithm is a method used to find the best values for the parameters of a machine learning model, minimizing a specific loss function and improving performance.
Overfitting
Overfitting occurs when a machine learning model performs well on the training data but fails to generalize to new, unseen data.
P
Pca
Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional representation.
Precision
In machine learning, precision is a measure of the accuracy of a classification model, defined as the ratio of correctly predicted positive samples to the total predicted positive samples.
Precision-Recall Curve
A Precision-Recall Curve is a graphical representation of the performance of a classification model at different classification thresholds.
Principal Component Analysis
Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space while preserving the most relevant information.
Principal Component Analysis (Pca)
Principal Component Analysis (PCA) is a technique used for dimensionality reduction by projecting data onto a lower-dimensional space.
R
Random Forest
A random forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model.
Recall
Recall is a metric used to measure the ability of a classification model to find all the relevant instances in a dataset.
Receiver Operating Characteristic (Roc) Curve
A Receiver Operating Characteristic (ROC) Curve is a graphical representation of the performance of a classification model at different classification thresholds.
Recommender System
A Recommender System is an information filtering system that predicts and suggests items or content to users based on their preferences and previous interactions.
Recurrent Neural Network
A recurrent neural network (RNN) is a type of artificial neural network designed to process sequential data by using an internal memory.
Recurrent Neural Network (Rnn)
A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data by using the concept of memory.
Regression
Regression is a supervised learning task that involves predicting a continuous numeric value based on input features.
Regularization
Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function.
Reinforcement Learning
Reinforcement learning is a type of machine learning that involves an agent learning to make decisions in an environment to maximize a reward signal.
Roc Curve
The ROC Curve is a graphical representation of the tradeoff between the true positive rate and the false positive rate.
S
Semi-Supervised Learning
Semi-supervised learning is a machine learning approach that combines labeled and unlabeled data to improve the performance of a model.
Sigmoid
The sigmoid function is a mathematical function that maps any real-valued number to a value between 0 and 1.
Stochastic Gradient Descent (Sgd)
Stochastic Gradient Descent (SGD) is a type of gradient descent algorithm that updates model parameters using a random subset of the training data.
Supervised Learning
Supervised learning is a machine learning task that involves learning a function from labeled training data to make predictions or decisions on new, unseen data.
Support Vector Machine
Support Vector Machine (SVM) is a supervised learning algorithm that separates data points by finding the optimal hyperplane that maximizes the margin between different classes.
Support Vector Machine (Svm)
A Support Vector Machine (SVM) is a supervised learning algorithm that can be used for classification or regression tasks.
Support Vector Machines
Support Vector Machines (SVM) is a machine learning algorithm that separates instances into different classes by finding the best possible hyperplane.
Svm
Support Vector Machines (SVMs) are a popular machine learning method for classification and regression tasks.
T
Tensor
In machine learning, a tensor is a mathematical object that is a generalization of scalars, vectors, and matrices, and is used to represent multi-dimensional arrays.
Testing Data
Testing Data is a portion of data used to evaluate the performance of a machine learning model, to assess its accuracy and generalization abilities.
Time Series Analysis
Time Series Analysis is a field of machine learning that focuses on analyzing and forecasting data points or observations collected over a continuous time interval.
Training Data
Training Data is a portion of data used to train a machine learning model, consisting of input data and corresponding output or target values.
Training Set
In machine learning, the training set is a subset of data used to train a machine learning model by adjusting its parameters based on the provided input and output examples.
Transfer Learning
Transfer learning is a machine learning technique where a pretrained model is used as a starting point for a new task.
U
Underfitting
Underfitting occurs when a machine learning model is too simple or has not been trained enough to capture the underlying patterns in the data.
Unsupervised Learning
Unsupervised learning is a machine learning task that involves learning patterns or relationships in unlabeled data.
V
Validation Data
Validation Data is a portion of data used to tune the hyperparameters of a machine learning model and assess its performance during training.
Validation Set
In machine learning, the validation set is a subset of the data used to evaluate the performance of a trained model and tune its hyperparameters.
Variance
In statistics and machine learning, variance is a measure of how much the values in a dataset vary from the mean value.
W
Word Embedding
Word embedding is a technique used to represent words or phrases as numerical vectors in a high-dimensional space, allowing for the analysis and processing of textual data.
X
X-Axis
The x-axis is the horizontal axis in a coordinate system, used to represent the independent variable or predictor.
Xgboost
XGBoost is an optimized gradient boosting machine learning library that is widely used for supervised learning tasks.
Y
Y-Axis
The y-axis is the vertical axis in a coordinate system, used to represent the dependent variable or response.
Z
Zero-Padding
Zero-padding is a technique used to increase the size of an input by adding zeros to the beginning or end of a sequence.