Machine Learning Algorithms

Articles related to machine learning algorithms will generally provide a taxonomy for the various models. They will give you a set of twenty algorithms along with their brief description of how it works, but you won’t learn anything practical out of them. It is a list of different algorithms which you can pick for your business model, but without any context or information as to how you should do so. This guide will fill up the void for you.

Not only will you receive the complete taxonomy and an insight into various machine learning algorithms, but also a process by which you will be able to distinguish between the various algorithm families and finally settle upon one or two options out of a host of many algorithms available.

A machine learning algorithm is the process through which the computer uses training data to generate the model that will be able to make decisions on new, unseen data.

Understanding the difference between algorithm and model has proven to be problematic for many aspiring machine learning practitioners. Here’s an analogy to keep in mind: the algorithm is the process, while the model is the outcome. The algorithm is used to generate the model; subsequently, it is used to predict new instances. In case the prediction fails, you may have to revise your algorithm or your data.

Model training is achieved through repeatedly using the algorithm on a data set while fine-tuning the model parameters to optimize performance on the data set; the performance here is measured via some metric that gives you an idea about the distance to perfection – that is known as the loss function. In other words, the objective of a machine learning algorithm is loss minimization.

Here are three packages that you can use in Python for implementing machine learning algorithms in practice: scikit-learn contains most traditional machine learning algorithms in a consistent API; XGBoost and LightGBM contain implementations of gradient boosting machines that run efficiently.

The 4 Types of Machine Learning Algorithms

But before jumping into individual algorithms, you need the map.

Based on only one criteria – how does the algorithm learn? – machine learning algorithms fall into four categories:

Supervised learning – you give the algorithm labeled examples (input and expected output), and it learns to solve the task (learn the mapping). This type of learning is used in solving most business-oriented tasks.

Unsupervised learning – the same thing, but with unlabeled data. Useful for data exploration and clustering, as well as compression.

Reinforcement learning – the algorithm makes decisions based on rewards or punishment received. Its correctness should be evaluated by cumulative results, and not by individual steps taken.

Self-supervised learning – the algorithm generates labels from the unlabeled dataset. That’s how such modern models as GPT and BERT were trained. Knowing this category is useful even if you don’t plan to create advanced AI from scratch, as these models can be fine-tuned to your task due to this type.

As mentioned before, all three players have three types presented in the top three SERP positions. Adding self-supervised as a fourth one isn’t excessive or pedantic – it’s just necessary.

Supervised Learning Algorithms

This is where most people will be spending most of their effort. You have your labeled dataset. You need to make a prediction. But what method do you choose?

The task in all supervised learning problems can fall into two categories, either regression or classification. Remember this distinction when we discuss each machine learning method listed below.

Linear Regression

The linear regression algorithm is used to make predictions where the output is numeric on an infinite scale using a linear combination of input variables.

The output value is continuous and represents, for instance, the cost of a house, money made in sales, or ambient temperature. Linear regression becomes the algorithm of choice when you think that there might be some sort of linear connection between inputs and outputs.

When to use it: Need a continuous target variable, relationship appears to be linear, and need to communicate the model to someone that does not know anything about XGBoost.

When to skip it: Highly non-linear relationship or interaction effects that were not engineered by hand.

Scikit-learn: LinearRegression(). Takes about five lines of code. 

Logistic Regression

Although the term implies otherwise, logistic regression is a classifier.

The algorithm estimates the probability that an instance belongs to a certain class using the output of the linear model through a sigmoidal curve, resulting in output within the range [0,1]. The user selects a threshold, most often 0.5, and classifies based on which side of the threshold they fall into.

When to use it: If you are performing binary classification, need well-calibrated probabilities, and want your system to be interpretable, you are dealing with logistic regression.

When to skip it: When classes are not linearly separable or the number of features is too high and interacts in non-linear ways.

Decision Trees

Decision trees ask yes/no questions about the value of features in the data, guiding each observation down branches based on their answers, until they end up at a leaf with a predicted label.

The beauty lies in the simplicity of interpretation. Print out your tree and walk your non-expert coworker through how a certain prediction was reached, and few machine learning algorithms will give you this ability. CART, C4.5, and ID3 are the primary decision tree algorithms; scikit-learn implements CART.

When to use it: when you must explain the reasoning behind predictions, and when your dataset has various types of variables. Decision trees also scale very well in terms of memory consumption.

When to skip it: When the tree is deep and prone to overfitting. It’s rare for someone to use a standalone decision tree in production; instead, it serves as an elementary component of other methods like random forest and boosting. When using the algorithm in production as-is, set maximum tree depth to something low.

Random Forest

Random forests train hundreds or thousands of decision trees using random subsets of both your data and the selected features. Then it combines all trees’ predictions by taking an average (for regression problems) or majority voting (for classification problems).

That’s how it works. The main trick is that random forests are randomized intentionally to prevent correlation between individual decision trees. This is bagging or bootstrap aggregating.

When to use it: your dataset is structured, you’re looking for a decent level of accuracy with low effort of tuning hyperparameters, and you’d like to have a way of measuring feature importances. Random forest is one of the best performers in dealing with missing values.

When to skip it: you aim for maximum accuracy using tabular data – in this scenario, gradient boosting will likely outperform other algorithms.

Gradient Boosting — XGBoost, LightGBM, CatBoost

This is the technique which dominates Kaggle competitions for tabular data sets. Year after year.

And the way the machine learning algorithm does it is by creating models sequentially and not in parallel, where the next model is built to fix the errors committed by the previously constructed set.

Three libraries dominate this space in 2026:

• XGBoost — the original production-grade gradient boosting library; fast, regularized, handles sparse data well
• LightGBM — Microsoft’s variant; histogram-based binning makes it significantly faster on large datasets
• CatBoost — Yandex’s contribution; handles categorical features natively without manual encoding

When to use it: If your data is structured and you require maximum accuracy while being open to hyperparameter tuning. XGBoost and LightGBM are the default choice in many machine learning competitions and for production tasks on structured datasets.

When to skip it: If your data is unstructured (images, text, audio). Such methods require that you have already extracted features, unlike CNNs and transformers that learn spatial and temporal relationships.

Support Vector Machines (SVM)

In an SVM, the optimal boundary, or the hyperplane, is the one which creates the maximum distance between the two groups. Points that lie on the boundary are termed as support vectors and nothing else matters when it comes to defining the line. 

SVM handles non-linear boundaries by making use of a kernel function (the RBF kernel being most commonly employed), which maps the data into high dimensional space where there exists a linear separation boundary.

When to use it: Data is high dimensional and has few examples (text categorization, bioinformatics), or when a solid theory-based approach is desired. SVMs were the de facto classification algorithm until the advent of deep learning.

When to skip: The amount of data is large. SVMs have poor scalability – their running time is approximately quadratic to the number of examples (~50k+).

k-Nearest Neighbors (k-NN)

k-NN does not learn a model during training – it only memorizes the data samples used for training. During inference, it looks for the k closest neighbors of your input within the training dataset and determines the output label based on majority vote.

The fact that k-NN is a lazy learning algorithm means that it makes zero assumptions about the distribution of the data used for training. However, it needs to pay for that luxury by searching through all training data at inference time.

When to use it: Small data sets, simple baseline models, recommendation algorithms (because they require a nearest neighbor search anyway), problems where geometry matters.

When to skip: Big data sets (it will be slow), high-dimensional data (the curse of dimensionality), memory constraints.

Naive Bayes

The Naïve Bayes classifier is based on Bayes’ Theorem which estimates the probabilities of classes from the observation of the values of the features; however, one major assumption is made about the features – that they are independent. Although independence is extremely rare in real life (that’s why naïve), most of the time it does not affect the results much.

When to use it: Text categorization, text filtering, sentiment detection. Despite the simplicity of this algorithm, its performance on text datasets is rather impressive and it is considered one of the best text classifiers because of its speed and efficiency especially on smaller datasets. Additionally, if online learning is necessary for some reason, this is the choice.

When to skip: Tasks requiring feature interaction.

Neural Networks — Multilayer Perceptron (MLP)

The multilayer perceptron creates a stack of layers, where each layer consists of neurons performing the calculation of a weighted sum of inputs with a subsequent application of a non-linear function (ReLU by default as of 2026). It is this non-linearity that enables the model to learn patterns that cannot be learned by any linear model.

Backpropagation is the method used for training the model — it involves calculating the contribution of each weight to the loss with the chain rule and then applying gradient descent to adjust all weights accordingly.

When to use it: When dealing with medium and large-sized datasets containing complex patterns or mixed tabular features; when gradient boosting algorithms have been exhausted but further improvements are required.

When to skip: Small-sized datasets (not enough data to train a neural network), situations where interpretability is important.

Unsupervised Learning Algorithms

Label-less. No ground truth. You’re asking the algorithm to discover any patterns in your dataset without help. The three key problems are clustering (clustering similar items), dimensionality reduction (reducing features to describe data), and association rule discovery (what goes with what).

K-Means Clustering

The k-means clustering algorithm assigns each observation to one out of k groups based on the centroid of the group that it belongs to. Centroids represent the geometric center of clusters and serve as the mean position of observations within them. The algorithm iterates by allocating observations to their closest centroids and then computing the centroids.

The major flaw: You have to set the value of k before implementing the algorithm. Typically, k-means is run several times using different values of k and selecting the most optimal number based on the elbow method or silhouette coefficient.

When to use it: Clustering customers, documents, images. K-means works quickly and can deal with millions of observations.

When to skip: Clusters of non-circular shapes (k-means assumes clusters to be somewhat circular), data with many outliers, absence of justification for a chosen value of k.

DBSCAN

DBSCAN (Density-based spatial clustering of applications with noise) defines clusters as dense areas of points divided by areas with sparse points. You don’t need to pre-define the number of clusters with DBSCAN, and more importantly, it identifies low density points as noise (outliers), which is not possible with k-means.

When to use it: Outlier detection, spatial clustering, when you want to deal with datasets with unusual cluster formations and need to find the outliers.

When to skip: Data with high dimensions becomes hard to measure reliably and very large datasets where DBSCAN’s computational cost is prohibitive.

Gaussian Mixture Models (GMMs)

Where k-means where each observation belongs to just one cluster, a GMM defines the likelihood of a point being associated with every cluster. The observations are described by a Gaussian distribution mixture, whereby the means, variances, and mixing coefficients of all the distributions are learned from the data.

The ability to assign point probabilities for multiple clusters makes a GMM better suited to clustering problems where boundaries between clusters are fuzzy, as in a situation where an individual may belong to two different market segments.

When to use it:  When you need probabilistic cluster membership, overlapping clusters, or you want a generative model of your data distribution.

Principal Component Analysis (PCA)

Principal component analysis lowers the number of dimensions of your dataset while maintaining maximum variability. It identifies the axes that maximize variation in the data, and it transforms the data to lie on this lower-dimensional space defined by these axes.

PCA is almost exclusively applied as a pre-processing tool in real-life applications. You would reduce high-dimensional data using PCA before passing it to a slower algorithm. Or you might use it to plot high-dimensional data on a 2D chart.

When to use it: You’re doing pre-processing of high dimensional data prior to training, you want to visualize some high dimensional data, you have highly correlated data to reduce.

When to skip: Interpretation of individual variables is important since it combines features in its components.

t-SNE

t-SNE (t-distributed Stochastic Neighbor Embedding) is a technique for reducing dimensions for visualization purposes. It is capable of projecting high dimensional data into either two or three dimensions while maintaining the neighborhood structure such that points which are near each other in the high dimensional space remain nearby in the 2D projection.

Honest caveat: t-SNE should not be used for anything else other than visualization. The x-y axes in t-SNE do not mean anything and you should not make any quantifiable conclusions based on t-SNE output. It should be used to understand the data qualitatively, not quantitatively.

When to use it: Data visualization – clustering in high dimensional space, especially in cases where embeddings are used.

When to skip: Anything else, especially tasks where you will be using the projections as inputs in other algorithms. In those cases, stick to PCA or similar techniques.

Apriori — Association Rule Mining

The Apriori algorithm discovers combinations of items that often occur together in transactional databases. An example of such a combination would be: “Those customers who purchase bread and peanut butter also purchase jelly 73% of the time.” 

When to use it: Analysis of shopping baskets; recommendation systems; fraud detection through identifying suspicious transactions; web clickstream analysis.

When to skip: Not applicable to non-transactional data; this algorithm is specifically created for lists of item type data.

Reinforcement Learning Algorithms

Reinforcement Learning differs in structure from Supervised Learning and Unsupervised Learning. In reinforcement learning, there is no dataset; rather, an agent learns by performing actions within an environment and seeing their results. If an action performed is beneficial, it receives a reward, and a penalty for any negative actions. The objective is to discover a policy of mapping between states and actions that maximize reward.

Q-Learning

Q-Learning is a value-based reinforcement learning algorithm that learns a Q-function. This function is essentially a table of scores showing the expected total score from performing all actions in each possible state. After learning this table of values, it simply suffices to choose the action with the highest value from it for any state encountered by the agent.

When to use it: Action space is discrete (e.g., chess, Atari games, grid worlds), state space is not too large to allow explicit storage of the Q-table.

When to skip: Action space is continuous (e.g., robot arm joint movements, trading positions), or state space is too large for an explicit Q-table.

Policy Gradient Methods — PPO and REINFORCE

Policy gradient techniques learn the policy itself instead of trying to estimate a value function. The likelihoods of the actions taken are changed depending on how well or poorly those actions turned out compared to expectations.

The Proximal Policy Optimization (PPO) technique is the leading algorithm in this category in 2026. It’s the technique behind RLHF – reinforcement learning from human feedback – which is the step in training that made general language models ChatGPT-style assistants.

When to use it: If you have a continuous action space, a very complicated environment, or want to fine-tune a language model using human feedback.

REINFORCE is the earliest policy gradient algorithm; a little less sophisticated and less stable than PPO.

Actor-Critic Methods

Actor-critic is an approach that uses policy gradients and value based reinforcement learning at the same time. The actor learns a policy while the critic learns a value function to evaluate how good it would be to be in a certain state. This helps reduce variance of the policy updates, one of the largest disadvantages of policy gradients.

The two types of actor-critics used are A2C (Advantage Actor Critic) and A3C (Asynchronous Advantage Actor Critic). All of the modern approaches to Deep Reinforcement Learning fall into this category.

When to use it: Robotics, Continuous Control Tasks anywhere you need stable policy learning in complex environments.

Self-Supervised and Semi-Supervised Learning Algorithms

These two categories often get collapsed into a footnote. They shouldn’t be — understanding them is now genuinely necessary for working with 2026-era ML.

Self-Supervised Learning

In self-supervised learning, a model trains on unlabeled data by creating its own labels based on the data structure. There are two broad categories of this:

Self-prediction: The model receives a portion of the input and tries to predict the rest of the sequence. For example, autoregressive models like GPT attempt to predict the next token based on all the tokens before it; conversely, masked language models like BERT predict masked tokens based on context. The text itself serves as the label – no need for human annotation.

Contrastive learning: The model receives two copies of the same input (one original and one augmented version) and understands that their representations should be similar, whereas different input’s representations should diverge. The two most famous methods for images are SimCLR and MoCo.

That’s how LLaMA, GPT-4o, BERT, and basically all other state-of-the-art models get their initial pre-trained model in 2026. While you won’t build such models yourself, you’ll use them extensively in fine-tuning, and understanding the nature of self-supervised pre-training helps explain why such a small amount of labeled data suffices for adaptation.

Semi-Supervised Learning

This technique involves a combination of labeled data and much larger amounts of unlabeled data. The main idea is the presence of informative structures in the unlabeled data, even if they are not clear in terms of their labels.

Self-training – the most straightforward semi-supervised technique: label the data using training from the labeled set, then incorporate new pseudo-labels into the training procedure for the same model. Iterate.

Label propagation – a graph-based algorithm which distributes labels based on proximity in the feature space.

When to use it: You have large amounts of unlabeled data but labeling is costly (e.g., in healthcare or legal cases). Semi-supervised learning can dramatically reduce annotation costs when done carefully.

Ensemble Learning Algorithms

Ensemble methods utilize many models together to improve prediction performance beyond that possible with one model alone. The basic idea is that each model makes different types of mistakes, and averaging their predictions corrects those mistakes.

Bagging — Random Forest

Now, you have been introduced to random forest as well, which is nothing but bagging of decision trees. This means that the trees must not be the same. You create differences among trees by training each one randomly with a bootstrap sample of the entire dataset and using random features.

Bagging lowers the variance without causing any increase in bias. Hence, it is preferred in case of base models which tend to overfit easily.

Boosting — AdaBoost and Gradient Boosting

Boosting builds its models sequentially. In essence, the subsequent models are trained solely on instances misclassified by their predecessors, creating a robust model from a series of weak ones.

AdaBoost — an older type of boosting (1995); assigns higher weights to wrongly classified instances to ensure the next classifier takes them into consideration.

Gradient Boosting — the more contemporary method; rather than weighting incorrectly classified instances, a new decision tree is trained to predict the loss function residuals of the ensemble. It happens to be the same as performing gradient descent in the space of functions, which was the key innovation behind this method.

XGBoost, LightGBM, and CatBoost are different implementations of gradient boosting, with additional engineering improvements.

Stacking

Stacking involves building a “meta-model” on top of the outputs of several different models. The base models (level 0) generate predictions using the training set, which becomes the input for the meta-model (level 1) to learn how to optimally use those predictions.

It’s important to note that stacking finds its way to Kaggle competitions much more frequently than it does to applications outside the realm of competition. In other words, there is a trade-off between increased model complexity and improved performance.

Deep Learning Algorithms

Deep Learning is a part of machine learning that applies neural networks with numerous layers. It’s not a different type of algorithm; they’re still ML algorithms – just algorithms that create feature representations themselves, instead of receiving them from outside.

The advantage these algorithms have over others is that they are able to discover hierarchies of features on their own by analyzing input data: in the case of a convolutional neural network, it can be done using pixels → edges → shapes → objects in a CNN, or characters → words → phrases → meaning in a transformer. No feature engineering required.

Convolutional Neural Networks (CNNs)

The CNN processes the input through the application of convolutions on local receptive fields of the input data. The input can be any grid-structured data, such as images, but also include audio spectrums and 1D sequences of values.

When to use it: Any image classification, object detection, medical image analysis, when spatial proximity is important.

Recurrent Neural Networks and LSTMs

The main strength of an RNN lies in its ability to keep the hidden state which propagates information from one step to another. Unlike traditional RNNs, LSTMs (Long Short-Term Memory Networks) manage to overcome the vanishing gradient issue that plagues simple RNNs; they are able to discover relationships over many time steps.

Honest disclaimer: In 2026, transformers have largely made RNNs and LSTMs obsolete for NLP problems. However, LSTMs work well on time series forecasting tasks such as energy consumption, stock market analysis, and sensor monitoring where efficiency is key.

When to use it: Time Series Forecasting, Sequence Tagging, Anomaly Detection in Sequences, Short Audio Processing.

Transformer Architecture

Transformer is the algorithmic architecture that is currently predominant in 2026.

Its emergence was seen in the paper published in 2017 titled “Attention is All You Need” by Vaswani et al., and within five years it had replaced RNN in NLP, CNN in many image-related applications (known as Vision Transformers), and is today the core architecture of any novel model.

This process relies on self-attention, whereby the model calculates a weighted combination of all the tokens in the sequence in relation to a particular token in the sequence. All this is done in parallel with regards to the whole sequence at once, which is unlike RNNs.

When to use it: Language tasks (classification, generation, translation, summarization) and increasingly image/multimodal tasks, whenever there exists a pre-trained transformer model on Hugging Face which can be fine-tuned in your domain.

Why this matters for practitioners: You may not train a transformer from scratch. However, fine-tuning a pre-trained transformer, whether it’s BERT for a classification task or LLaMA-based models for generation, is something practitioners need to be adept at today. The key is knowing how the pre-trained model was trained using self-supervision on a huge text corpus and then being able to fine-tune the pre-trained model using supervised fine-tuning.

How to Choose the Right Machine Learning Algorithm

Here is my honest take: Either most articles on choosing algorithms provide an irrelevant flow chart that doesn’t work for anything, or they are filled with a complex decision matrix that only a genius can figure out. But this is not what you need at the beginning of your journey.

What really helps in making an effective choice is asking five questions one after another.

Question 1: What type of output do you need?

This single question eliminates half the algorithms immediately.

• A number on a continuous scale → regression algorithms: linear regression, gradient boosting regressor, neural networks
• A category or label → classification algorithms: logistic regression, random forest, SVM, gradient boosting classifier
• Groups with no predefined labels → clustering: k-means, DBSCAN, GMMs
• The best sequence of actions → reinforcement learning: Q-learning, PPO

If you can’t clearly state your output type, you don’t have a well-defined problem yet. Go back to that step first.

Question 2: How much labeled data do you have?

• Thousands to millions of labeled examples → standard supervised learning; all doors are open
• Hundreds of labeled examples → smaller models: logistic regression, Naive Bayes, SVM, or fine-tuning a pre-trained transformer
• No labels at all → unsupervised: clustering, dimensionality reduction
• Few labels + lots of unlabeled data → semi-supervised; use self-training or label propagation to extend your labeled set
• Labels would be too expensive to collect at scale → self-supervised pre-training followed by fine-tuning on a small labeled subset

The transformer era has genuinely changed the answer to this question. In 2022, having 500 labeled examples for a text classification task was limiting. In 2026, fine-tuning BERT or a similar model on 500 examples often gets you 90%+ accuracy. Labeled data requirements have dropped significantly for language and image tasks.

Question 3: What type of data are you working with?

Data type is probably the most underrated selection criterion.

• Structured/tabular data → start with XGBoost or LightGBM; they win most structured-data benchmarks and require less preprocessing than neural networks
• Images or video → CNNs or Vision Transformers; don’t try to flatten images into tabular features
• Text or language → transformer-based models (BERT for understanding, GPT-style for generation); fine-tune from Hugging Face
• Time series → XGBoost with lag features for tabular-style time series, LSTMs for sequence-heavy data, or purpose-built libraries like Prophet for decomposition
• Graphs and networks → Graph Neural Networks; this is a specialized domain most practitioners won’t touch often

Here’s the thing that surprises many newcomers: gradient boosting doesn’t understand the word “before.” Feed it time-series data without engineering lag features yourself, and it’ll miss all the temporal dependencies. You have to build those features manually.

Question 4: Does interpretability matter?

This question doesn’t get asked enough, and skipping it creates problems downstream — especially in regulated industries.

• Interpretability is a hard requirement (healthcare, finance, legal, insurance) → logistic regression, linear regression, decision trees. You need to be able to explain every individual prediction in plain language.
• Interpretability is nice but not required → random forest gives you feature importance rankings; gradient boosting tools like SHAP can approximate explanations for black-box models
• Accuracy matters more than explanations → neural networks, deep gradient boosting, stacked ensembles; go for maximum performance

I think most practitioners underestimate how often interpretability constraints apply to their use case. A bank that can’t explain why a loan was denied faces regulatory exposure. A medical diagnosis tool that can’t explain its reasoning is genuinely dangerous, not just inconvenient. Know your compliance context before choosing a model.

Question 5: What are your compute and latency constraints?

The best-performing model is useless if it takes 3 seconds to respond to a user request or requires a GPU cluster to retrain weekly.

• Sub-millisecond inference on CPU → logistic regression, Naive Bayes, linear models; these are tiny and fast
• Standard inference (50–200ms), moderate compute → gradient boosting, random forest, shallow neural networks
• Batch processing, accuracy-first, GPU available → deep learning, large transformer fine-tunes
• Edge deployment (mobile, IoT) → quantized or distilled small models; this is a whole sub-field

XGBoost inference on a laptop is measured in microseconds per prediction. A BERT-class model takes 20–100ms on a GPU depending on sequence length. A GPT-scale model running on a managed API costs money per token. These aren’t equivalent choices; your serving infrastructure shapes your algorithm choice as much as your data does.

Machine Learning Algorithms and the 2026 Landscape

Three shifts have changed how practitioners think about algorithm selection compared to even 2022.

Transformers are now the default for unstructured data. If your use case requires anything text-based, image-based, or audio-based, a reasonable first step in 2026 is using a pre-trained transformer rather than training a CNN or RNN from scratch. Hugging Face’s model hub has pre-trained models for a myriad of specific purposes – medicine, law, satellite imagery, etc. The entry cost for top-notch performance in all these use cases has plummeted.

RLHF and DPO have entered the practitioner vocabulary. Nowadays, reinforcement learning from human feedback (PPO-based), along with direct preference optimization, is the norm when fine-tuning LLMs to behave in a certain way. You will come across both of these methods if you do work with language models. The reason for DPO gaining traction is its simplicity compared to RLHF.

AutoML has become practical for tabular tasks. Automation tools such as the AutoML from H2O.ai and AutoSklearn perform automated model selection, hyper-parameter tuning, and model ensembling over structured data. Here’s my honest opinion, try AutoML on any new structured dataset as your starting point, and if you find the results suitable for your use case, you don’t have to spend more time working on other aspects that AutoML cannot solve.

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Frequently Asked Questions

Wrapping Up

Now you have everything that you need: a taxonomy of what algorithms there are, and a framework for how to choose amongst them.

What really counts, here, is not the taxonomy but the decision framework. Everyone can rattle off the names of twenty different algorithms. It takes an expert to know that gradient boosting will perform better than random forest when it comes to benchmark problems in tables, that fine-tuning BERT on five hundred labeled examples will be better than building your own custom classifier from scratch, and that compliance with a regulation requiring interpretability makes many approaches moot.

The most rapidly evolving line is the divide between “classical ML” and “foundation models”. Whether it is XGBoost applied to tabular data and/or transformers applied to fine-tuning tasks on text – all of this goes into the repertoire of an experienced machine learning practitioner – they are no longer different universes.

Think small first. Test your basic model before adding unnecessary complexity. And whenever you have any doubts, apply AutoML to your tabular data and fine-tune pre-trained models on your text data set – leaving the rest for manual work.