Top 10 Machine Learning Algorithms Explained for Beginners

There are hundreds of machine learning algorithms, but a small set does most of the work in practice. Knowing these ten, what each does, where it shines and where it fails, lets you choose a sensible starting point for almost any problem. Each is described below in plain terms, and seven of them are then compared head to head on real data, because an algorithm's reputation is less useful than its measured results on your kind of problem.

If the difference between predicting a number and a category, or between learning with and without labels, is new, start with regression vs classification and supervised vs unsupervised learning.

Supervised algorithms

1. Linear regression. Predicts a number as a weighted sum of the inputs. Fast, interpretable and a strong baseline for regression; it assumes effects add up in a straight line, so it misses curves and interactions unless you add features for them.

2. Logistic regression. The classification counterpart: a weighted sum passed through an S-shaped function to give a probability. Well-calibrated probabilities and clear coefficients make it a standard in credit scoring and medicine. It draws straight decision boundaries.

3. k-nearest neighbours (k-NN). Classifies a new point by the majority label of its k closest training points. No training step at all, but prediction is slow on large data, it needs scaled features, and it struggles when there are many irrelevant features.

4. Naive Bayes. Applies Bayes' rule assuming features are independent given the class. Extremely fast and useful for text, but the independence assumption rarely holds, so its probabilities are often poorly calibrated.

5. Decision tree. A sequence of yes-or-no questions on features, learned from data. Easy to explain and handles mixed data types, but a single deep tree overfits badly.

6. Random forest. Hundreds of trees trained on random samples of rows and features, with their votes averaged. Robust, needs little tuning, and a strong default for tabular data. Ensemble learning explained shows how averaging reduces overfitting.

7. Gradient boosting. Trees built one after another, each correcting the errors of the previous ones. Usually the most accurate family on tabular data once tuned; implementations include XGBoost, LightGBM and scikit-learn's histogram-based boosting. XGBoost explained covers the details.

8. Support vector machine (SVM). Finds the boundary with the widest margin between classes and, with kernels, can draw curved boundaries. Strong on small to medium datasets with scaled features; training slows sharply as data grows.

Unsupervised algorithms

9. k-means clustering. Splits data into k groups around centre points, repeatedly assigning points to the nearest centre and moving the centres. Simple and fast; you choose k, and it assumes roughly round, similarly sized groups. Clustering algorithms compared covers alternatives.

10. Principal component analysis (PCA). Compresses many correlated features into a few new ones that keep most of the variation, for visualisation, noise reduction or faster models. Dimensionality reduction explained measures what it keeps and loses.

Neural networks deserve a mention as the family behind image, speech and language models. For those data types they dominate; for spreadsheet-style data, the tree ensembles above usually match or beat them, as the evidence below shows. Deep learning explained builds one from scratch.

A head-to-head benchmark

Seven of the ten are classifiers, so they can be compared directly. Here each runs with default settings on three real datasets, spam detection from word frequencies, classifying speech sounds, and separating gamma-ray signals from background in telescope data, using the same 5-fold cross-validation:

import pandas as pd
from sklearn.datasets import fetch_openml
from sklearn.ensemble import HistGradientBoostingClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

datasets = {"spam emails": (44, "1"), "speech sounds": (1489, "2"), "telescope events": (1120, "h")}
models = {
    "logistic regression": make_pipeline(StandardScaler(), LogisticRegression(max_iter=2000)),
    "k-nearest neighbours": make_pipeline(StandardScaler(), KNeighborsClassifier()),
    "naive Bayes": GaussianNB(),
    "decision tree": DecisionTreeClassifier(random_state=0),
    "random forest": RandomForestClassifier(n_estimators=200, random_state=0, n_jobs=-1),
    "gradient boosting": HistGradientBoostingClassifier(random_state=0),
    "support vector machine": make_pipeline(StandardScaler(), SVC()),
}
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
results = {}
for data_name, (data_id, positive) in datasets.items():
    data = fetch_openml(data_id=data_id, as_frame=True)
    X, y = data.data, (data.target == positive).astype(int)
    results[data_name] = {name: cross_val_score(model, X, y, cv=cv).mean() for name, model in models.items()}
table = pd.DataFrame(results)
table["average rank"] = table.rank(ascending=False).mean(axis=1)
print(table.sort_values("average rank").round(3).to_string())

                        spam emails  speech sounds  telescope events  average rank
gradient boosting             0.956          0.895             0.881         1.333
random forest                 0.953          0.912             0.880         1.667
support vector machine        0.932          0.844             0.868         3.667
k-nearest neighbours          0.909          0.876             0.839         4.333
decision tree                 0.914          0.866             0.816         4.667
logistic regression           0.924          0.750             0.790         5.667
naive Bayes                   0.821          0.760             0.727         6.667

What the numbers say:

• The two tree ensembles lead everywhere, within a point or so of each other, and each wins at least one dataset.
• The ranking of the rest changes with the data. Logistic regression comes fourth on spam, ahead of k-NN and the single tree, because word frequencies relate to spam in a fairly straight-line way, but it is last on speech sounds, where the pattern is strongly non-linear.
• A single decision tree trails the forest built from trees by 4 to 6 points, the gain from averaging.
• Naive Bayes is last on two of the three and never better than second-last, the price of its independence assumption on these features.

Speed matters too. On the machine used, logistic regression, naive Bayes and k-NN fitted in a fraction of a second, while the SVM became the slowest to train on the largest dataset, 19,020 rows, taking longer than the random forest.

What larger studies add

Three datasets are a small sample, so it helps that large studies agree. A study of 179 classifiers on 121 datasets found random forest versions the most likely to be the best (Fernández-Delgado et al., JMLR 2014), and a 2022 benchmark on 45 datasets found tree-based models such as XGBoost and random forests remain state of the art on medium-sized tabular data, ahead of deep learning (Grinsztajn et al., 2022). No algorithm wins everywhere, but for tabular data, tree ensembles are the sensible first serious model.

Choosing where to start

The scikit-learn documentation includes a flowchart for choosing estimators by problem type and data size, a useful companion to this table (scikit-learn: choosing the right estimator), and its user guide documents each model's strengths and parameters (scikit-learn: supervised learning).

Linear and logistic regression, decision trees, random forests, SVMs, k-means, hierarchical clustering and DBSCAN are all covered in the machine learning core module of the Data Science course.

Sources

• Supervised learning (scikit-learn user guide) — scikit-learn documentation
• Choosing the right estimator (scikit-learn) — scikit-learn documentation
• Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? — Journal of Machine Learning Research, 2014
• Why do tree-based models still outperform deep learning on tabular data? — NeurIPS 2022 Datasets and Benchmarks