Machine learning models uncover patterns and make predictions on new data by learning from examples. At the core of machine learning are the algorithms that power these capabilities. As machine learning becomes a pervasive tool across industries, it is important for both practitioners and business leaders to have a foundational understanding of common algorithms, their applications, and tradeoffs.
In this blog post, we provide an introduction to the basic machine learning algorithms across the major categories of supervised learning, unsupervised learning, and reinforcement learning. We discuss leading examples of each algorithm type and considerations for selecting appropriate modeling techniques. Understanding machine learning requires examining the algorithms that drive everything, from product recommendations to self-driving cars.
Types of Machine Learning Algorithms
Basic machine learning algorithms are often grouped into three primary categories:
Supervised Learning Algorithms
Supervised learning algorithms build models that learn mappings from input features to target outputs based on labeled training data.
Linear regression is used for predicting continuous numerical outcomes like house prices based on input variables like square footage and location. It models the target as a linear weighted combination of the inputs. Simple linear regression involves a single input, while multiple regression accommodates many explanatory variables. Key assumptions are linear relationships, statistically independent errors, and homoscedasticity.
Logistic regression is suited for binary classification tasks like medical diagnosis classification based on patient symptoms and medical test results. It models class probabilities using the logistic sigmoid function. Logistic regression makes few assumptions about distribution of inputs but does assume linear separable classes. It outperforms linear regression when the actual decision boundary is nonlinear.
K-nearest neighbors classify points based on the majority class among the k most similar instances. Performance depends heavily on distance metrics and the value of k. Benefits include simplicity and nonparametric flexibility. Drawbacks involve computational costs for large training sets.
Decision trees recursively partition data points into subgroups based on value tests. Each leaf node represents a class outcome. Visualization provides intuitive explanations for predictions. However, pruning is needed to prevent overfitting on individual trees.
Random forests overcome single-tree overfitting by averaging predictions across many de-correlated trees created from random bootstrap samples and feature subsets. The ensemble improves robustness and accuracy.
Support vector machines maximize margin hyperplanes in high-dimensional space to separate classes. Kernel tricks implicitly map input data into expanded feature spaces to handle nonlinear problems. SVMs effective for complex classification tasks.
Bootstrap aggregation (bagging) trains each model on random data samples to reduce variance vs. a single estimator. Model averaging smoothes predictions by combining outputs from diverse estimators like different basic machine learning algorithms.
Boosting incrementally adds models to correct predecessors’ errors by focusing on difficult instances. Adaptive boosting (AdaBoost) reweights data based on errors. XGBoost algorithm is popular for structured data. Gradient boosting generalizes to different loss functions.
Stacking trains, a meta-learner algorithm to combine predictions from multiple base algorithms. It enables blending very different models together such as SVM, random forests, and neural networks in an ensemble.
Unsupervised Learning Algorithms
Clustering algorithms group unlabeled data points based on similarity features. K-means clustering partitions observations into k clusters based on distances to cluster means. Hierarchical clustering builds tree representations by iteratively merging or splitting clusters based on distances. Performance depends heavily on distance metrics. Soft clustering methods assign gradual rather than hard cluster memberships.
Dimensionality reduction transforms data from high-dimensional spaces to lower dimensions while preserving key information. Principal component analysis uses orthogonal transforms to convert correlated variables into linearly uncorrelated principal components capturing maximal variance. Other techniques include non-negative matrix factorization and t-distributed stochastic neighbor embedding (t-SNE).
Density estimation algorithms model the probability distribution of data variables. Histogram and kernel density estimation techniques partition and smooth data densities. Mixture models like Gaussian mixture models and latent Dirichlet allocation model overall distributions as mixtures of simpler distributions. This supports exploration of heterogeneity in the data.
Association rule learning extracts interesting co-occurrence relationships between variable values within datasets, powering recommendation engines. The Apriori algorithm identifies frequent item sets meeting minimum support thresholds. FP-growth algorithms build compact data structures called FP-trees to efficiently mine large transaction databases.
Principal component analysis (PCA) applies orthogonal transforms to convert possibly correlated variables into linearly uncorrelated principal components. Components are ranked by the amount of data variance explained. PCA facilitates visualization, analysis, and learning in lower dimensional space.
Non-negative matrix factorization (NMF) expresses data as additive combinations of non-negative basis components. This yields more interpretable parts-based representations than PCA. NMF is commonly used for multivariate data like images and text documents.
t-SNE maps multidimensional data into lower dimensions for visualization using probabilities that represent pairwise similarities. t-SNE excels at preserving local neighbor structure in reduced space.
Reinforcement Learning Algorithms
Reinforcement learning agents learn optimal behavioral policies by taking actions and receiving feedback rewards or penalties. Markov decision processes formalize sequential decision-making problems and algorithms like dynamic programming solve them through backward induction. Monte Carlo methods sample episodes of experience to estimate long-term returns.
Temporal difference learning increments state value functions based on backup deltas between temporally successive states to bootstrap learning. Q-learning off-policy TD algorithm is commonly used. On-policy SARSA learns from experiences following the current policy. Actor-critic methods maintain separate policy and value functions.
Deep reinforcement learning combines neural networks with RL, yielding breakthrough applications like AlphaGo. Policy gradient methods directly learn stochastic policies via gradient ascent on expected rewards. Deep Q-networks use deep neural nets to represent Q-values for complex problems with raw state inputs like images.
Overall, modern machine learning leverages a rich spectrum of algorithmic techniques tailored to data characteristics and modeling goals. No single best approach exists. The art lies in matching algorithms to problems and intelligently combining methods to achieve optimal performance.
Comparing and Tuning Algorithms
Choosing the right basic machine learning algorithms for a problem requires understanding their relative strengths and weaknesses:
- Simpler linear models like regression provide transparency but cannot capture nonlinear relationships as well as multilayer neural networks.
- However, deep neural nets require extensive training data and computing resources to tune millions of parameters. Shallow learners like decision trees can train faster on limited data.
- Clustering techniques like k-means scale well but performance depends heavily on distance metrics and cluster number k. Hierarchical clustering is flexible but computationally expensive.
Proper performance evaluation guides selection. Metrics like R-squared, log loss, confusion matrices, and cluster validation indices quantify model quality. Diagnostic tools identify bias, overfitting, and input dependencies.
Tuning algorithms by optimizing hyperparameters like learning rate, tree depth, and regularization strength via grid/random search improves results. Automated optimization frameworks like Bayesian hyperparameter tuning are emerging. Overall, combining complementary algorithms in ensembles boosts robustness.
Cutting Edge Advances
Ongoing research is advancing basic machine learning algorithms:
- Explainable AI techniques like LIME and SHAP open the black box of complex models like deep neural networks to provide human-understandable explanations.
- Distributed computing frameworks like TensorFlow and PyTorch scale training over clusters of machines to handle massive datasets.
- Transfer learning and multi-task learning leverage knowledge gained from related tasks to speed up learning with limited data.
- Novel convolutional and graph network architectures model new data types like images, text, meshes, and molecules.
- Techniques to embed fairness, accountability, transparency, and ethics into algorithm design help address societal concerns.
Mastering basic machine learning algorithms and how they work start with studying core algorithms for supervised learning, unsupervised learning, and reinforcement learning. Linear regression logistics regression, decision trees, neural networks, k-means, principal component analysis, and Q-learning represent an essential starter toolkit. Of course, entire sub-fields delve into these techniques in far greater mathematical and computational depth. But foundational proficiency empowers tackling more advanced real-world applications. The algorithms introduced here provide the building blocks for cutting-edge artificial intelligence systems transforming every sector of the economy.