Choosing Your First Conventional ML Baseline

Overview

Questions

• What counts as a sensible baseline or comparison model?
• Which conventional models belong in my starter model basket?

Objectives

• Define both a trivial reference baseline and a practical model basket.
• Choose an initial model based on task type, data shape, interpretability, and time available.
• Distinguish between a first baseline model and a stronger comparison model.

Why start with conventional models

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When you first start modelling a real problem, you usually need a small basket of models that are understandable, quick to run, and easy to compare.

This lesson is designed to support participant viewing during the workshop. Use it as a reference while you work through the notebook activities.

Good first choices are driven by four practical questions:

• What task type has already been identified?
• What does the data look like?
• Can you explain the result?
• Can you build and evaluate it today?

For this reason, conventional machine learning is often the right place to start. It gives you a reference point before you decide whether you need more complex feature engineering or feature learning.

Discussion

Confirm the modelling goal

Before selecting a model, write down:

• your task type;
• your target or prediction goal;
• what a wrong answer looks like;
• whether interpretability matters.

Two kinds of baseline

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It helps to separate two different baseline ideas.

1. Trivial reference baseline

This is the simple strategy you should try to beat.

• Regression: predict the mean or median every time.
• Classification: predict the majority class every time.
• Clustering: compare against a simple no-clustering description or a very rough grouping.

This step matters because it answers a basic question: is the machine learning model learning anything useful beyond a trivial rule?

2. First machine learning baseline

This is the first real model you build.

A model basket

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You do not need to try every model. You only need a small set of sensible options.

Core starter models

These are the safest first models for most projects at this stage.

• Linear Regression for continuous targets when you want a quick, interpretable baseline.
• Logistic Regression for categorical targets when you want a strong, simple classifier.
• Decision Tree when interpretability matters or you want to inspect simple decision rules.
• k-Nearest Neighbours as a useful comparison model when local similarity may matter.
• K-Means for simple exploratory clustering.

Useful additions when needed

• Naive Bayes for sparse text or count-based text features.
• Random Forest when you want a stronger tree-ensemble comparison with limited tuning. It combines many trees built in parallel and averages their predictions.
• Support Vector Machine when margins or high-dimensional spaces may matter.
• XGBoost or Gradient Boosting when you want a stronger boosted-tree comparison and have time for the extra setup and tuning. Unlike random forests, boosting builds trees in sequence so that each new tree tries to correct errors made by the earlier ones.

In most cases, start with the simpler models first. Random forests, support vector machines, and boosted trees are often more useful as comparison models after you already have a simple baseline in place.

The sections below are split by model type so they are easier to find from the lesson index.

Linear Regression

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• Best for: small or medium tabular regression.
• Why start here: quick, interpretable, easy to explain.

Key points: The model represents the outcome as a weighted sum of the input variables. Learning means adjusting those weights to minimize prediction error on a continuous target.

Docs: LinearRegression

Logistic Regression

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• Best for: small or medium tabular classification.
• Why start here: strong baseline with a simple decision boundary.

Key points: The model uses a weighted combination of the inputs and then applies a logistic function to turn that score into a class probability. Learning means adjusting the weights so the predicted probabilities better match the observed labels, usually by minimizing cross-entropy loss; it can be viewed as a neural network with no hidden layer.

Docs: LogisticRegression

Challenge

Logistic regression beyond binary classification

Logistic regression is often first taught for binary classification.

• Is logistic regression only for binary classification?
• If not, how can it be extended to multiclass classification?
• How is the model learned from the training data?

Answer

No. Logistic regression is often introduced first for binary classification, but it is not limited to only two classes.

For multiclass classification, the output layer can use a softmax activation so the model produces one probability per class, with those probabilities summing to 1. In that form, logistic regression becomes a simple multiclass linear classifier.

The model is learned by adjusting its weights to minimize cross-entropy loss, usually with a gradient descent based optimization algorithm. That is the same general optimization idea used when training neural networks.

Decision Tree

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• Best for: interpretable tabular problems where simple rules matter.
• Why start here: easy to inspect and discuss.

Key points: A decision tree represents the model as a sequence of if-then splits. Learning means choosing splits that make the resulting groups purer for classification or more consistent for regression.

Docs: DecisionTreeClassifier

Challenge

Decision trees, impurity, and linear boundaries

When fitting a decision tree for classification:

• What does it mean to say that a split makes the child nodes “purer”?
• How can that impurity be measured?
• Is a decision tree a linear classifier?

Answer

For classification, a split is useful when it makes the resulting child nodes contain more cases from a single class than before. In other words, the split reduces class mixing within each node.

That impurity can be measured in different ways. Two common choices are Gini impurity and entropy. Both try to quantify how mixed the classes are inside a node, and the tree algorithm searches for splits that reduce that impurity as much as possible.

No, a decision tree is not a linear classifier. A linear classifier uses a single linear boundary in the feature space, while a decision tree builds a sequence of axis-aligned splits that creates a piecewise, rule-based decision boundary.

k-Nearest Neighbours

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• Best for: a simple comparison model when local similarity matters.
• Why start here: gives a useful contrast with linear methods.

Key points: k-nearest neighbours predicts from the nearest training cases, where “nearest” is defined by a distance measure in the feature space. For classification, it takes a majority vote across the k neighbours; for regression, it can take their average.

Docs: KNeighborsClassifier

Challenge

k-nearest neighbours for regression

k-nearest neighbours is often introduced as a classification method.

• Can it also be used for regression?
• How do we decide which neighbours count as the “nearest” ones?

Answer

Yes. For regression, k-nearest neighbours can predict by averaging the target values of the k nearest neighbours instead of taking a majority vote.

The “nearest” neighbours are chosen using a distance measure in the feature space, such as Euclidean distance. That is why scaling can matter a lot for k-nearest neighbours: variables on larger numeric scales can otherwise dominate the distance calculation.

K-Means

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• Best for: exploratory unlabeled grouping.
• Why start here: simple clustering workflow.

Key points: K-Means represents each cluster by a centre point. Learning means repeatedly assigning cases to the nearest centre and updating the centres until the grouping stabilizes.

Docs: KMeans

Naive Bayes

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• Best for: sparse text classification and count-based features.
• Why start here: good simple baseline for bag-of-words style inputs.

Key points: The model treats features as separate pieces of evidence and combines them probabilistically for each class. Learning mostly means estimating how often features appear within each class from the training data.

Docs: MultinomialNB

Random Forest

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• Best for: a stronger tabular comparison after a first baseline.
• Why start here: many trees are averaged together, which usually improves stability over a single tree.

Docs: RandomForestClassifier

Related docs: Gradient boosting in scikit-learn

Challenge

Random forests and feature selection

Random forests are often introduced as a stronger tree-based comparison model.

• Can a random forest be used for feature selection?
• What advantage does a random forest have over a single decision tree?

Answer

Yes, a random forest can be used for feature selection in a rough, practical sense. Because it can estimate feature importance, it can help you identify variables that seem to contribute more to the predictions. That said, feature-importance scores are not a perfect or universal measure, so they should be treated as guidance rather than proof that a feature is truly important.

The main advantage of a random forest over a single decision tree is stability. A single tree can change a lot with small changes in the training data and can overfit easily. A random forest reduces that risk by fitting many trees on different samples and feature subsets, then combining their predictions by voting or averaging.

Support Vector Machine

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• Best for: high-dimensional comparison problems with small to medium sample sizes.
• Why start here: can work well when margins and similarity structure matter, though it may be slower to fit.

Key points: A support vector machine makes its decision using a small set of key training cases called support vectors, rather than all training points or the nearest neighbours. With a kernel function, it can measure similarity between a new case and those support vectors in a transformed feature space, which is why SVMs are often useful for small-sample, high-dimensional problems, although fitting can become slow because the optimization often involves a large kernel matrix.

Docs: SVC

Challenge

Support vector machines beyond classification

Support vector machines are often introduced as classification models.

• Does an SVM make predictions from all training points?
• What role do the support vectors and kernel function play?
• Can an SVM also be used for regression?
• Why can SVMs become slow to train, and which hyperparameters matter?

Answer

An SVM does not usually rely equally on all training points. Its decision rule is mainly determined by a smaller set of influential cases called support vectors, which are the training points closest to the boundary or margin.

The kernel function measures similarity between a new data point and the support vectors. This lets the model behave as if it were working in a higher-dimensional feature space, so it can represent more flexible nonlinear boundaries without explicitly constructing all those new features. That is one reason SVMs are often used when the sample size is small but the number of input variables is large.

Yes. SVM ideas can also be used for regression, often called support vector regression or SVR. Instead of separating classes, the model tries to fit a function that predicts a continuous outcome while still being controlled by margin-like ideas and support vectors.

Training can become computationally expensive because kernelized SVMs often need to compute and optimize over a large kernel matrix that stores similarities between many pairs of training cases. Important hyperparameters include C, which controls the trade-off between margin size and training errors, and any kernel parameters, such as gamma for an RBF kernel or the degree for a polynomial kernel.

XGBoost

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• Best for: a stronger tuned tree-ensemble comparison.
• Why start here: often performs well, but usually needs more tuning and setup time.

Key points: XGBoost represents the model as a sequence of small trees added one after another. Learning means each new tree focuses on the mistakes or residual patterns left by the earlier trees.

Docs: XGBoost Python package

Install note: XGBoost is a separate package rather than part of scikit-learn, so it needs to be installed before use.

Challenge

Random forests versus XGBoost

Random forests and XGBoost are both tree-ensemble methods, so they can look similar at first.

• What is the main difference between how a random forest and XGBoost build their trees?
• Why might XGBoost perform better, but also need more tuning?

Answer

The main difference is how the ensemble is built. A random forest grows many trees independently in parallel on different resampled versions of the data and different feature subsets, then combines their predictions by voting or averaging.

XGBoost builds trees sequentially, not independently. Each new tree is trained to focus on the mistakes or residual patterns left by the earlier trees, so the model improves step by step.

Because of that sequential boosting strategy, XGBoost can often produce stronger predictive performance than a random forest. The trade-off is that it usually has more settings that matter in practice, such as the learning rate, tree depth, and the number of boosting rounds, so it often needs more careful tuning.

Rule of thumb

Move in this order:

1. define the trivial reference baseline;
2. fit one simple conventional machine learning baseline;
3. only then compare a stronger or more specialised model.

This keeps the lesson focused on what is new at this stage: not identifying the task type again, but choosing a sensible first model for that task.

What not to do

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Do not pick a model because it sounds powerful.

Common poor choices include:

• choosing a deep neural network before establishing a baseline;
• comparing many models without a reference point;
• using a complex model that cannot be interpreted or debugged;
• picking a model that does not match the target type.

When the basket is not enough

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If a simple conventional model misses important structure, the next step is usually one of these:

• improve the features you provide to the model;
• compare a stronger conventional model;
• move to neural networks or transfer learning if the model needs to learn features automatically.

That is why the next lessons look at where to search for further improvement: better engineered features, stronger neural-network approaches, and feature learning when the model needs to learn useful representations more automatically.

Challenge

Choose a baseline sequence

Complete the sentence:

“My task is __________, my trivial reference baseline is __________, and my first ML baseline is __________ because __________.”

If you are planning a later development step, add one more line:

“My next justified upgrade would be __________ because __________.”

Example answers

• “My task is regression, my trivial reference baseline is predicting the mean house price, and my first ML baseline is Linear Regression because the data are tabular and I need an interpretable starting point.”
• “My task is classification, my trivial reference baseline is the majority class, and my first ML baseline is Logistic Regression because I want a simple, explainable model before comparing anything stronger.”
• “My task is sparse text classification, my trivial reference baseline is the majority class, and my first ML baseline is Naive Bayes because it works well with simple text-count features and is quick to test.”

A minimal Python pattern

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The code below shows the structure of a simple baseline process for a classification task.

If you want a concrete mental model while reading this code, map it to the worked example on horse blinking: measured input features come in, the model predicts the blink-related class or outcome, and the baseline is checked against a trivial reference first.

PYTHON

from sklearn.datasets import load_iris
from sklearn.dummy import DummyClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

iris = load_iris(as_frame=True)
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

reference_model = DummyClassifier(strategy="most_frequent")
reference_model.fit(X_train, y_train)
reference_predictions = reference_model.predict(X_test)

baseline_model = LogisticRegression(max_iter=1000)
baseline_model.fit(X_train, y_train)
baseline_predictions = baseline_model.predict(X_test)

print("Reference accuracy:", accuracy_score(y_test, reference_predictions))
print("Baseline accuracy:", accuracy_score(y_test, baseline_predictions))

The point of this example is not the specific score. The point is the process:

• split the data;
• fit a trivial reference;
• fit a simple baseline;
• compare them with one primary metric.

Key points

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Key Points

• Choose the task type before choosing the algorithm.
• A good starter model basket includes both simple baselines and one or two stronger comparison options.
• Conventional models are usually the right first step for structured or limited data.
• Stronger models should be added for a reason, not because they sound more advanced.