0) Introduction

XGBoost is a gradient-boosting library for supervised learning that is widely used for classification and regression. In Python, the most common estimators are XGBClassifier and XGBRegressor. The official docs describe XGBoost as an optimized gradient boosting library that provides parallel tree boosting for fast and accurate modeling.

1) What XGBoost does

XGBoost builds an ensemble of trees sequentially. It starts with a simple model, measures the current errors, then adds a new tree that tries to correct those errors. The final prediction is the sum of the contributions from many trees. This is the same boosting idea you saw in Gradient Boosting, but XGBoost adds strong engineering and regularization features that make it especially effective in practice.

For classification, the model predicts a class or class probabilities. For regression, it predicts a numeric value. In the Python API, these tasks are handled by xgboost.XGBClassifier and xgboost.XGBRegressor.

2) Why XGBoost is popular

XGBoost is popular because it often performs very well on tabular data, handles nonlinear relationships, supports regularization, and offers CPU and GPU training options. The official docs also note support for GPU training through the device parameter.

In practice, XGBoost is often chosen when:

• you want strong predictive performance
• your features interact in complex ways
• you are working with structured data
• you are willing to tune the model carefully

Those strengths come from the boosting framework plus the additional controls exposed in XGBoost’s parameter system.

3) XGBoost vs classical Gradient Boosting

Both are boosting methods, but XGBoost is more specialized and more optimized. Classical gradient boosting in scikit-learn builds trees stage by stage using gradients of the loss, while XGBoost adds its own parameter system, regularization controls, optimized training implementation, and broader system support.

A useful practical distinction is this:

• Gradient Boosting in scikit-learn is a great way to learn the idea
• XGBoost is a more advanced production-oriented implementation of boosted trees

That is an inference from the official descriptions of each library’s boosting framework and capabilities.

4) Core intuition

Think of XGBoost as building many small corrective trees. Each new tree tries to improve what the current ensemble is still getting wrong. Over time, the model becomes stronger by adding many small improvements instead of relying on one large tree.

This means the important ideas are:

• boosting: add trees one after another
• weak learners: usually shallow trees
• learning rate: control how much each new tree changes the model
• regularization: reduce overfitting

XGBoost’s official parameters page separates configuration into general, booster, and learning-task parameters, which reflects this design.

5) Important XGBoost parameters

Some of the most important parameters in practice are:

• n_estimators: number of trees
• learning_rate: shrinkage factor
• max_depth: maximum tree depth
• subsample: row sampling
• colsample_bytree: feature sampling per tree
• reg_alpha: L1 regularization
• reg_lambda: L2 regularization
• objective: learning task
• eval_metric: evaluation metric
• device: CPU or GPU choice in modern XGBoost 

These parameters matter because they control model complexity, how aggressively the model learns, and how much randomness and regularization are applied.

6) Learning rate and number of trees

The interaction between learning_rate and n_estimators is one of the most important tuning choices.

• smaller learning_rate usually means safer, slower learning
• smaller learning_rate often requires more trees
• larger learning_rate can converge faster but may overfit more easily

This is a standard consequence of boosted additive models, and XGBoost exposes both controls directly in its parameter system.

7) Tree depth and overfitting

max_depth controls how complex each tree can become.

• small depth → simpler learners
• large depth → more expressive learners
• very large depth → higher overfitting risk

Because XGBoost adds many trees, allowing each one to become too large can make the full model overly complex. That follows directly from the booster parameter controls in the official docs.

8) Subsampling and column sampling

XGBoost can randomly sample both rows and features.

• subsample controls the fraction of training rows used
• colsample_bytree controls the fraction of features used per tree

These are useful regularization tools because they reduce correlation and can help generalization. They are official booster parameters in XGBoost.

9) Regularization in XGBoost

One reason XGBoost is powerful is that it includes explicit regularization terms.

• reg_alpha adds L1 regularization
• reg_lambda adds L2 regularization

These are documented booster parameters in XGBoost and are part of what distinguishes it from simpler boosting implementations.

10) Do you need feature scaling?

For tree-based XGBoost models, feature scaling is usually not necessary, because tree splits are threshold-based rather than distance-based. This is an inference from the fact that XGBClassifier and XGBRegressor are boosted tree estimators, like other tree-based methods.

So unlike KNN or SVM, you usually do not start with StandardScaler for tree-based XGBoost.

Part I — Installation

11) Install the required libraries

pip install xgboost scikit-learn numpy pandas matplotlib

The XGBoost Python docs describe the Python package as having multiple interfaces, including the scikit-learn interface, which is what we will use here.

Part II — First Classification Example

12) Train an XGBClassifier

We will use the Breast Cancer dataset from scikit-learn.

import numpy as np
import pandas as pd

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from xgboost import XGBClassifier

# Load dataset
data = load_breast_cancer()
X = data.data
y = data.target

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

# Build model
model = XGBClassifier(
    n_estimators=200,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="binary:logistic",
    eval_metric="logloss",
    random_state=42
)

# Train
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)

# Evaluate
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

train_test_split is the standard scikit-learn utility for splitting data into train and test subsets, classification_report summarizes precision, recall, and F1-style metrics, and confusion_matrix evaluates classification results in matrix form.

13) What this code does

This workflow:

• loads the data
• creates training and test sets
• fits an XGBoost classifier
• predicts test labels
• evaluates the results with accuracy, confusion matrix, and classification report

The classification metrics used here are standard scikit-learn evaluation tools, and XGBClassifier is part of the official XGBoost Python API.

14) Predicting probabilities

Like many classifiers, XGBoost can return probabilities.

proba = model.predict_proba(X_test[:5])
print(proba)

This is useful when you care not only about the predicted class, but also about how confident the model is. XGBClassifier exposes standard classifier methods like fit, predict, and probability-style prediction in the Python API.

Part III — Important Classification Parameters

15) The most useful ones to tune

For XGBClassifier, the most common parameters to tune are:

• n_estimators
• learning_rate
• max_depth
• subsample
• colsample_bytree
• reg_alpha
• reg_lambda
• sometimes min_child_weight and gamma for additional tree regularization 

These parameters control how many trees the model uses, how large the trees are, how aggressively it learns, and how strongly it regularizes.

16) A safer starting configuration

A practical baseline often looks like this:

model = XGBClassifier(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    reg_alpha=0.0,
    reg_lambda=1.0,
    objective="binary:logistic",
    eval_metric="logloss",
    random_state=42
)

This is not a universal best configuration, but it is a sensible starting point based on the official XGBoost booster parameters and common boosting practice.

Part IV — Feature Importance

17) Built-in feature importance

XGBoost models can report feature importances.

import pandas as pd

importance = pd.Series(model.feature_importances_, index=data.feature_names)
print(importance.sort_values(ascending=False))

This can help you see which variables the model used most strongly. The scikit-learn style XGBoost estimators expose feature_importances_ in the Python API.

18) Plot feature importance

import matplotlib.pyplot as plt

importance = importance.sort_values(ascending=True)

plt.figure(figsize=(8, 6))
importance.plot(kind="barh")
plt.title("XGBoost Feature Importance")
plt.xlabel("Importance")
plt.ylabel("Feature")
plt.show()

This is useful for interpretation, though model-based importance should still be treated carefully as a model summary rather than proof of causality. That caution is an inference based on how tree-based importance values are constructed.

Part V — Hyperparameter Tuning

19) Tune with GridSearchCV

Scikit-learn’s GridSearchCV performs an exhaustive search over parameter combinations.

from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier

param_grid = {
    "n_estimators": [100, 200, 300],
    "learning_rate": [0.01, 0.05, 0.1],
    "max_depth": [3, 4, 5],
    "subsample": [0.8, 1.0],
    "colsample_bytree": [0.8, 1.0]
}

grid = GridSearchCV(
    estimator=XGBClassifier(
        objective="binary:logistic",
        eval_metric="logloss",
        random_state=42
    ),
    param_grid=param_grid,
    cv=5,
    scoring="accuracy",
    n_jobs=-1
)

grid.fit(X_train, y_train)

print("Best Parameters:", grid.best_params_)
print("Best CV Score:", grid.best_score_)

best_model = grid.best_estimator_
y_pred = best_model.predict(X_test)
print("Test Accuracy:", accuracy_score(y_test, y_pred))

This is one of the best practical ways to choose a strong XGBoost configuration.

20) What to tune first

A good order of priority is often:

1. learning_rate
2. n_estimators
3. max_depth
4. subsample
5. colsample_bytree
6. regularization terms like reg_alpha and reg_lambda

That ordering is a practical inference based on the official XGBoost parameter groups and the way boosting models typically behave.

Part VI — XGBoost Regression

21) Train an XGBRegressor

Now let us use XGBoost for regression.

import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from xgboost import XGBRegressor

# Synthetic data
rng = np.random.RandomState(42)
X = np.sort(5 * rng.rand(200, 1), axis=0)
y = np.sin(X).ravel()

# Add noise
y[::5] += 0.5 - rng.rand(40)

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

model = XGBRegressor(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="reg:squarederror",
    random_state=42
)

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print("MSE:", mean_squared_error(y_test, y_pred))
print("R²:", r2_score(y_test, y_pred))

XGBRegressor is the regression counterpart in the XGBoost Python API, while mean_squared_error and r2_score are standard scikit-learn regression metrics.

22) Plot regression predictions

X_plot = np.linspace(X.min(), X.max(), 500).reshape(-1, 1)
y_plot = model.predict(X_plot)

plt.figure(figsize=(8, 6))
plt.scatter(X, y, label="Data")
plt.plot(X_plot, y_plot, linewidth=2, label="XGBoost prediction")
plt.xlabel("X")
plt.ylabel("y")
plt.title("XGBoost Regression")
plt.legend()
plt.show()

This kind of example is useful for seeing how boosting can model a nonlinear curve from many tree-based corrections. That is a direct consequence of XGBoost’s boosted-tree formulation.

Part VII — Full Real Workflow

23) End-to-end classification workflow

Step 1: Load data

import pandas as pd

df = pd.read_csv("your_data.csv")

Step 2: Separate features and target

X = df.drop("target", axis=1)
y = df["target"]

Step 3: Split data

from sklearn.model_selection import train_test_split

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

Step 4: Build model

from xgboost import XGBClassifier

model = XGBClassifier(
    objective="binary:logistic",
    eval_metric="logloss",
    random_state=42
)

Step 5: Tune parameters

from sklearn.model_selection import GridSearchCV

param_grid = {
    "n_estimators": [100, 200],
    "learning_rate": [0.05, 0.1],
    "max_depth": [3, 4, 5],
    "subsample": [0.8, 1.0],
    "colsample_bytree": [0.8, 1.0]
}

grid = GridSearchCV(
    model,
    param_grid=param_grid,
    cv=5,
    scoring="accuracy",
    n_jobs=-1
)

grid.fit(X_train, y_train)

Step 6: Evaluate

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

best_model = grid.best_estimator_
y_pred = best_model.predict(X_test)

print("Best Params:", grid.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

This follows the standard scikit-learn estimator workflow for train/test splitting, cross-validated search, and classification evaluation.

Part VIII — Categorical Data

24) XGBoost and categorical features

The official XGBoost docs note that one easy way to pass categorical data is through a dataframe using the scikit-learn interface, with columns explicitly marked as category dtype.

Example idea:

import pandas as pd
from xgboost import XGBClassifier

X["cat_feature"] = X["cat_feature"].astype("category")

model = XGBClassifier(
    tree_method="hist",
    enable_categorical=True,
    objective="binary:logistic",
    eval_metric="logloss"
)

This is a useful modern feature in XGBoost when your data includes categorical columns.

Part IX — GPU Support

25) Using GPU

The XGBoost docs state that to enable GPU support, you can set the device parameter to cuda or gpu in the Python API.

Example:

model = XGBClassifier(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    objective="binary:logistic",
    eval_metric="logloss",
    device="cuda",
    random_state=42
)

This can speed up training substantially on supported hardware.

Part X — How to Read Results

26) Classification metrics

For classification, the most common metrics are:

• accuracy
• precision
• recall
• F1-score
• confusion matrix

Scikit-learn provides classification_report and confusion_matrix for these tasks.

from sklearn.metrics import confusion_matrix, classification_report

print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

27) Regression metrics

For regression, common metrics include:

• MAE
• MSE
• RMSE
• R²

These are standard scikit-learn regression evaluation tools.

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np

mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print("MAE:", mae)
print("MSE:", mse)
print("RMSE:", rmse)
print("R²:", r2)

Part XI — Strengths and Weaknesses

28) Strengths of XGBoost

XGBoost is strong because it offers:

• boosted trees with high predictive power
• strong regularization controls
• CPU and GPU support
• a scikit-learn-compatible interface
• support for categorical workflows in modern usage 

These features are part of why it remains a standard choice for many tabular machine learning tasks.

29) Weaknesses of XGBoost

Its main limitations are:

• it usually needs tuning
• it is less interpretable than a single tree
• training can become expensive with large search spaces
• careless parameter choices can overfit

These are practical consequences of using a flexible boosted ensemble with many interacting parameters.

Part XII — Common Mistakes

30) Using too high a learning rate

Bad:

model = XGBClassifier(
    n_estimators=500,
    learning_rate=0.5,
    objective="binary:logistic",
    eval_metric="logloss"
)

Better:

model = XGBClassifier(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="binary:logistic",
    eval_metric="logloss"
)

That advice follows from the role of shrinkage and stage count in boosted models.

31) Ignoring regularization

Many beginners tune only n_estimators and max_depth, but XGBoost also provides subsample, colsample_bytree, reg_alpha, and reg_lambda, which are important for controlling overfitting.

32) Searching too many combinations at once

GridSearchCV is exhaustive, so the search can become slow if the grid is too large. The scikit-learn docs explicitly describe it as trying all parameter combinations, and also note alternatives like RandomizedSearchCV.

So start with a modest grid, then refine around promising values.

Part XIII — Practical Advice

33) When should you use XGBoost?

Use XGBoost when:

• you want strong performance on structured data
• you are comfortable tuning hyperparameters
• nonlinear relationships matter
• you want a robust boosting method with mature tooling 

34) When should you avoid it?

Be cautious when:

• interpretability is your top priority
• you need the simplest possible baseline
• you do not have time to tune
• a lighter model would already solve the problem

Those are practical tradeoffs of a powerful but flexible ensemble method.

35) Good default starting points

For binary classification:

XGBClassifier(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="binary:logistic",
    eval_metric="logloss",
    random_state=42
)

For regression:

XGBRegressor(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="reg:squarederror",
    random_state=42
)

These are sensible baselines based on the official XGBoost parameter families, not guaranteed best settings.

Part XIV — Mini Project Example

36) Predicting iris species with XGBoost

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from xgboost import XGBClassifier

# Load
data = load_iris()
X, y = data.data, data.target

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

# Model
model = XGBClassifier(
    objective="multi:softprob",
    num_class=3,
    eval_metric="mlogloss",
    random_state=42
)

# Search space
param_grid = {
    "n_estimators": [100, 200, 300],
    "learning_rate": [0.01, 0.05, 0.1],
    "max_depth": [3, 4, 5],
    "subsample": [0.8, 1.0],
    "colsample_bytree": [0.8, 1.0]
}

# Grid search
grid = GridSearchCV(
    model,
    param_grid=param_grid,
    cv=5,
    scoring="accuracy",
    n_jobs=-1
)

grid.fit(X_train, y_train)

# Evaluate
best_model = grid.best_estimator_
y_pred = best_model.predict(X_test)

print("Best Parameters:", grid.best_params_)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

This example combines XGBoost’s scikit-learn interface with scikit-learn’s split, search, and evaluation tools in a standard workflow.

Part XV — Summary

37) What you should remember

XGBoost is a powerful boosted-tree method for classification and regression. It builds trees sequentially, each one trying to improve the current model, and it provides practical controls for shrinkage, sampling, regularization, and hardware acceleration.

The most important practical rules are:

• feature scaling is usually not needed for tree-based XGBoost
• tune learning_rate and n_estimators together
• control complexity with max_depth
• use subsample and colsample_bytree
• do not ignore reg_alpha and reg_lambda
• use cross-validation for tuning 

38) Final ready-to-use template

from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import classification_report
from xgboost import XGBClassifier

# X, y = your data

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

model = XGBClassifier(
    objective="binary:logistic",
    eval_metric="logloss",
    random_state=42
)

param_grid = {
    "n_estimators": [100, 200],
    "learning_rate": [0.05, 0.1],
    "max_depth": [3, 4, 5],
    "subsample": [0.8, 1.0],
    "colsample_bytree": [0.8, 1.0]
}

grid = GridSearchCV(
    model,
    param_grid=param_grid,
    cv=5,
    scoring="accuracy",
    n_jobs=-1
)

grid.fit(X_train, y_train)

print("Best params:", grid.best_params_)
print("Test score:", grid.best_estimator_.score(X_test, y_test))

y_pred = grid.best_estimator_.predict(X_test)
print(classification_report(y_test, y_pred))

39) Practice exercises

Exercise 1

Train an XGBClassifier on the Iris dataset and report accuracy.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from xgboost import XGBClassifier

# Load Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

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

# Build model
model = XGBClassifier(
    n_estimators=200,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="multi:softprob",
    num_class=3,
    eval_metric="mlogloss",
    random_state=42
)

# Train
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)

# Evaluate
acc = accuracy_score(y_test, y_pred)

print("Accuracy:", acc)
print("\nConfusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

Exercise 2

Train XGBoost on the Breast Cancer dataset and display feature importances.

import pandas as pd
import matplotlib.pyplot as plt

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from xgboost import XGBClassifier

# Load dataset
data = load_breast_cancer()
X = data.data
y = data.target

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

# Build model
model = XGBClassifier(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="binary:logistic",
    eval_metric="logloss",
    random_state=42
)

# Train
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)

# Accuracy
print("Accuracy:", accuracy_score(y_test, y_pred))

# Feature importances
importance = pd.Series(model.feature_importances_, index=data.feature_names)
importance = importance.sort_values(ascending=False)

print("\nFeature Importances:\n")
print(importance)

# Plot feature importances
plt.figure(figsize=(10, 8))
importance.sort_values().plot(kind="barh")
plt.title("XGBoost Feature Importances")
plt.xlabel("Importance")
plt.ylabel("Feature")
plt.show()

Exercise 3

Tune learning_rate, n_estimators, and max_depth using GridSearchCV.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score
from xgboost import XGBClassifier

# Load data
iris = load_iris()
X = iris.data
y = iris.target

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

# Base model
model = XGBClassifier(
    objective="multi:softprob",
    num_class=3,
    eval_metric="mlogloss",
    random_state=42
)

# Parameter grid
param_grid = {
    "learning_rate": [0.01, 0.05, 0.1],
    "n_estimators": [100, 200, 300],
    "max_depth": [3, 4, 5]
}

# Grid search
grid = GridSearchCV(
    estimator=model,
    param_grid=param_grid,
    cv=5,
    scoring="accuracy",
    n_jobs=-1
)

# Train grid search
grid.fit(X_train, y_train)

# Best model
best_model = grid.best_estimator_

# Predict
y_pred = best_model.predict(X_test)

# Results
print("Best Parameters:", grid.best_params_)
print("Best Cross-Validation Score:", grid.best_score_)
print("Test Accuracy:", accuracy_score(y_test, y_pred))

Exercise 4

Compare a RandomForestClassifier with an XGBClassifier.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier

# Load data
iris = load_iris()
X = iris.data
y = iris.target

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

# Model 1: Random Forest
rf_model = RandomForestClassifier(
    n_estimators=200,
    random_state=42
)

# Model 2: XGBoost
xgb_model = XGBClassifier(
    n_estimators=200,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="multi:softprob",
    num_class=3,
    eval_metric="mlogloss",
    random_state=42
)

# Train both
rf_model.fit(X_train, y_train)
xgb_model.fit(X_train, y_train)

# Predict
y_pred_rf = rf_model.predict(X_test)
y_pred_xgb = xgb_model.predict(X_test)

# Accuracy
acc_rf = accuracy_score(y_test, y_pred_rf)
acc_xgb = accuracy_score(y_test, y_pred_xgb)

print("Accuracy of Random Forest:", acc_rf)
print("Accuracy of XGBoost:", acc_xgb)

Exercise 5

Train an XGBRegressor on a nonlinear synthetic regression dataset.

import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from xgboost import XGBRegressor

# Create synthetic nonlinear data
rng = np.random.RandomState(42)
X = np.sort(5 * rng.rand(200, 1), axis=0)
y = np.sin(X).ravel()

# Add noise
y[::5] += 0.5 - rng.rand(40)

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

# Build regressor
model = XGBRegressor(
    n_estimators=300,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    objective="reg:squarederror",
    random_state=42
)

# Train
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)

# Evaluate
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse)
print("R² Score:", r2)

# Plot prediction curve
X_plot = np.linspace(X.min(), X.max(), 500).reshape(-1, 1)
y_plot = model.predict(X_plot)

plt.figure(figsize=(8, 6))
plt.scatter(X, y, label="Data")
plt.plot(X_plot, y_plot, linewidth=2, label="XGBoost prediction")
plt.xlabel("X")
plt.ylabel("y")
plt.title("XGBoost Regression on Nonlinear Data")
plt.legend()
plt.show()

Final summary

What each exercise teaches

• Exercise 1: basic XGBoost classification
• Exercise 2: feature importance analysis
• Exercise 3: hyperparameter tuning with cross-validation
• Exercise 4: comparison between Random Forest and XGBoost
• Exercise 5: nonlinear regression with XGBoost