Lesson 28 · Python For Machine Learning

Understanding Gradient Boosting with LightGBM in Python for Advanced Machine Learning

Welcome! In this lesson you are going to explore gradient boosting, a powerful way to make accurate predictions. You will use LightGBM, a popular Python…

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CoursePython For Machine Learning
Lesson28 of 16
Video9 min
FormatJupyter notebook · 18 code cells

scikit-learn pandasLightGBMNumPy Matplotlib

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Gradient Boosting with LightGBM: Step by Step#

Welcome! In this lesson you are going to explore gradient boosting, a powerful way to make accurate predictions. You will use LightGBM, a popular Python package, and work with real data. Let us start simple, then build up to a mini-project using the Titanic dataset.

Get ready to learn and experiment!

What is Gradient Boosting?#

Gradient boosting is a machine learning technique. It builds many small decision trees to make better predictions.

LightGBM is a tool that makes this process fast and efficient.

You will use this power for real problems soon!

# Hide any warnings to keep the output clean
import warnings
warnings.filterwarnings('ignore')

# Install LightGBM (do this only once!)
# If you are in Colab or Jupyter, run this cell. It may take a minute.
# !pip install lightgbm --quiet

Data setup#

You will use the Titanic dataset to predict if a passenger survived. Let us load the data using pandas and look around.

import pandas as pd

url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
df = pd.read_csv(url)
print('Shape:', df.shape)
df.head()

Shape: (891, 12)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

# What does the target look like? Who survived?
df['Survived'].value_counts()

Survived
0    549
1    342
Name: count, dtype: int64

# Show missing values by column
df.isnull().sum()

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

Preparing the data#

You need to clean the data before using machine learning. Let us keep the important columns and remove or fill in missing values.

# Keep only important columns for our first model
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
data = df[columns].copy()

# Fill missing age with the mean, embarked with mode
data['Age'] = data['Age'].fillna(data['Age'].mean())
data['Embarked'] = data['Embarked'].fillna(data['Embarked'].mode()[0])

data.head()

	Pclass	Sex	Age	SibSp	Fare	Embarked	Survived
0	3	male	22.0	1	7.2500	S	0
1	1	female	38.0	1	71.2833	C	1
2	3	female	26.0	0	7.9250	S	1
3	1	female	35.0	1	53.1000	S	1
4	3	male	35.0	0	8.0500	S	0

# Convert text to numbers so the model can read it
data['Sex'] = data['Sex'].map({'male': 0, 'female': 1})
data['Embarked'] = data['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})

# Split into features X and label y
X = data.drop('Survived', axis=1)
y = data['Survived']

# Split into train and test sets
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
)

Building your first LightGBM model#

Let us train a simple model to predict survival. You will use the LightGBM classifier.

import lightgbm as lgb

# Create the model
model = lgb.LGBMClassifier(random_state=42)

# Train the model
model.fit(X_train, y_train)

[LightGBM] [Info] Number of positive: 268, number of negative: 444
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 195
[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838
[LightGBM] [Info] Start training from score -0.504838
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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LGBMClassifier(random_state=42)

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# Make predictions on the test set
y_pred = model.predict(X_test)
print('Predicted survival:', y_pred[:10])

Predicted survival: [0 0 0 1 1 1 1 0 1 1]

# Check accuracy
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', round(accuracy, 3))

Accuracy: 0.838

Understanding model performance#

Accuracy tells you how often you were right.

But sometimes you want more detail. Let us look at other ways to check the model.

# Use a confusion matrix for details
from sklearn.metrics import confusion_matrix
conf = confusion_matrix(y_test, y_pred)
print('Confusion matrix:')
print(conf)

Confusion matrix:
[[91 14]
 [15 59]]

# Try predicting on new, made-up passengers using input()
new_pclass = int(input("Passenger class (1, 2, or 3): "))
new_sex = int(input("Sex (0 for male, 1 for female): "))
new_age = float(input("Age: "))
new_sibsp = int(input("Number of siblings/spouses aboard: "))
new_parch = int(input("Number of parents/children aboard: "))
new_fare = float(input("Fare: "))
new_embarked = int(input("Embarked (0=S, 1=C, 2=Q): "))
new_data = [[new_pclass, new_sex, new_age, new_sibsp, new_parch, new_fare, new_embarked]]
prediction = model.predict(new_data)[0]
print("Predicted Survival:", prediction)

Predicted Survival: 1

Improving your LightGBM model#

You can often improve models by tuning parameters. Let us try changing how many trees the model builds.

# Increase the number of trees (estimators)
better_model = lgb.LGBMClassifier(n_estimators=200, random_state=42)
better_model.fit(X_train, y_train)
better_pred = better_model.predict(X_test)
print('Improved Accuracy:', round(accuracy_score(y_test, better_pred), 3))

[LightGBM] [Info] Number of positive: 268, number of negative: 444
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 195
[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838
[LightGBM] [Info] Start training from score -0.504838
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Improved Accuracy: 0.821

# Find which features are most important
import numpy as np
import matplotlib.pyplot as plt
features = X_train.columns
importances = better_model.feature_importances_
sorted_idx = np.argsort(importances)[::-1]

plt.figure(figsize=(8,4))
plt.bar(features[sorted_idx], importances[sorted_idx])
plt.title('Feature Importance')
plt.ylabel('Importance')
plt.xlabel('Feature')
plt.show()

No description has been provided for this image

Challenge exercise time!#

Try changing the model's settings, or create a new feature. For example, you could:

Add a feature for 'child' if Age < 16
Try different numbers for n_estimators
Use only a few features to see what changes

Can you beat the previous accuracy?

Recap: What have you learned?#

Gradient boosting combines many simple models for better predictions.
LightGBM makes it fast and easy in Python.
Data cleaning and feature selection are key.
You trained and improved a model step by step.
You even made predictions for new data!

You are on your way to building smart solutions.

Try it yourself and learn more!#

Experiment with your own ideas and different datasets.

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Leave a comment about what you will build next!

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	boosting_type	'gbdt'
	num_leaves	31
	max_depth	-1
	learning_rate	0.1
	n_estimators	100
	subsample_for_bin	200000
	objective	None
	class_weight	None
	min_split_gain	0.0
	min_child_weight	0.001
	min_child_samples	20
	subsample	1.0
	subsample_freq	0
	colsample_bytree	1.0
	reg_alpha	0.0
	reg_lambda	0.0
	random_state	42
	n_jobs	None
	importance_type	'split'