There is no single best algorithm (the “no free lunch” theorem) — but there are good defaults. Start simple, compare candidates with the same cross-validation, and combine models when it pays.

Algorithm	Boundary / shape	Scaling needed	Interpretability	Good for	Weak spots
Linear / logistic regression	Linear	Yes (when regularised)	High (coefficients)	Baselines, high-dimensional sparse data, probabilities	Non-linear effects unless engineered
k-NN	Any (local)	Yes	Medium (show neighbours)	Small, low-dimensional data	Slow predictions, high dimensions
Naive Bayes	Linear-ish	No	Medium	Text, tiny data, speed	Correlated features, poor probabilities
SVM (RBF)	Any (smooth)	Yes	Low	Small/medium data, many features	Large n, needs tuning, no native probabilities
Decision tree	Axis-aligned steps	No	High (rules)	Explanations, rules	High variance
Random forest	Axis-aligned, averaged	No	Medium	Strong default for tabular data	Size, extrapolation
Gradient boosting	Axis-aligned, additive	No	Medium	Best accuracy on most tabular problems	Tuning, overfits without early stopping
Neural networks	Any	Yes	Low	Images, audio, text, very large data	Data-hungry, tuning, compute

A practical order of attack

Baseline
DummyClassifier / DummyRegressor and any existing business rule.
Simple model
Regularised linear or logistic regression in a pipeline — fast, often surprisingly strong, and a sanity check for leakage.
Tree ensemble
Random forest, then gradient boosting (HistGradientBoosting / LightGBM / XGBoost) with early stopping.
Unstructured data
Pretrained deep models (fine-tuning or embeddings) — see Deep Learning and Generative AI.
Pick with constraints
Accuracy is one criterion; latency, memory, interpretability, calibration and maintenance matter too.

Compare candidates fairly

Python

from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import HistGradientBoostingClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

X, y = load_breast_cancer(return_X_y=True)
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)      # same folds for every model
candidates = {
    "logistic": make_pipeline(StandardScaler(), LogisticRegression()),
    "knn": make_pipeline(StandardScaler(), KNeighborsClassifier()),
    "svc": make_pipeline(StandardScaler(), SVC()),
    "random forest": RandomForestClassifier(random_state=0),
    "hist gboost": HistGradientBoostingClassifier(random_state=0),
}
for name, model in candidates.items():
    s = cross_val_score(model, X, y, cv=cv, scoring="roc_auc")
    print(f"{name:14} {s.mean():.3f} ± {s.std():.3f}")

Output

logistic       0.995 ± 0.006
knn            0.981 ± 0.009
svc            0.996 ± 0.005
random forest  0.992 ± 0.007
hist gboost    0.994 ± 0.005

On this small, clean dataset the linear model is as good as anything — a reminder to always try the simple option. Differences inside one standard deviation are usually noise (see Hypothesis Tests & Significance for proper comparisons).

Voting and stacking

Method	How	Notes
Hard voting	Majority of predicted labels	Simple; ignores confidence
Soft voting	Average predicted probabilities	Usually better; needs reasonably calibrated models
Stacking	A meta-model learns how to combine base models' out-of-fold predictions	Most flexible; base models should be diverse

Python

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier, StackingClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier

X, y = make_classification(n_samples=600, n_features=20, n_informative=6, random_state=1)
base = [("lr", LogisticRegression()), ("rf", RandomForestClassifier(random_state=0)),
        ("knn", KNeighborsClassifier()), ("nb", GaussianNB())]

for name, est in base:
    print(name, round(cross_val_score(est, X, y, cv=5).mean(), 3))
vote = VotingClassifier(base, voting="soft")
stack = StackingClassifier(base, final_estimator=LogisticRegression(), cv=5)
print("soft vote", round(cross_val_score(vote, X, y, cv=5).mean(), 3))
print("stacking", round(cross_val_score(stack, X, y, cv=5).mean(), 3))

Output

lr 0.95
rf 0.937
knn 0.937
nb 0.915
soft vote 0.95
stacking 0.957

From ML to neural networks

Logistic regression is a one-neuron network: weighted sum → sigmoid → log-loss. A multi-layer perceptron stacks layers of such units with non-linear activations, so it can learn non-linear boundaries and its own features:

Python

from sklearn.datasets import make_moons
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.neural_network import MLPClassifier

X, y = make_moons(n_samples=400, noise=0.2, random_state=0)
print(round(cross_val_score(LogisticRegression(), X, y, cv=5).mean(), 3))    # 0.875 ← one linear boundary
mlp = MLPClassifier(hidden_layer_sizes=(16, 16), max_iter=3000, random_state=0)
print(round(cross_val_score(mlp, X, y, cv=5).mean(), 3))                     # 0.948 ← hidden layers bend the boundary

For anything beyond small experiments, neural networks are built in PyTorch or similar — backpropagation, optimisers, CNNs, RNNs and transformers are covered in Deep Learning.

How would you choose a model for a tabular classification problem?

What is stacking and how do you avoid leakage in it?

What does the no-free-lunch theorem say?