Machine Learning Tutorials, Courses and Certifications
What is XGBoost?¶
- XGBoost (Extreme Gradient Boosting) is a machine learning algorithm that implements the gradient boosting framework.
- It is designed for efficiency, flexibility, and performance, making it suitable for structured or tabular data.
Key Concepts¶
Boosting:
- A technique where models are built sequentially, with each new model trying to correct errors from the previous ones.
- Weak learners, usually decision trees, are combined to form a strong predictive model.
Gradient Boosting:
- Utilizes gradient descent to minimize the loss function, adjusting the model based on the gradients of errors.
- Incorporates a learning rate to control how much the model is updated with each iteration.
Regularization:
- XGBoost includes L1 (Lasso) and L2 (Ridge) regularization to prevent overfitting, ensuring better generalization to unseen data.
Handling Missing Values:
- The algorithm can handle missing data automatically, learning the best way to account for them during training.
Parallel Processing:
- Designed to utilize multiple CPU cores, speeding up the training process compared to traditional boosting methods.
Key Features¶
- High Performance: Often outperforms other algorithms in terms of speed and accuracy.
- Flexibility: Capable of handling various data types and suitable for both regression and classification tasks.
- Feature Importance: Provides insights into the importance of different features in making predictions, enhancing model interpretability.
Applications¶
- XGBoost is particularly effective with structured data, large datasets, and is widely used in data science competitions for its accuracy and efficiency. It is a favored choice for tackling various machine learning problems, making it a staple in the data scientist’s toolkit.
Let’s review Practically¶
XGBoost Algorithm¶
- Binray Classification -> objective=’binary:logistic’
- Multi Classification -> objective=’multi:softprob’
- Regression -> objective=’reg:linear’ or objective=’reg:squarederror’
XGBoost Binary Classification¶
In [15]:
!pip install xgboost
Requirement already satisfied: xgboost in c:\users\mehak\appdata\local\programs\python\python312\lib\site-packages (2.1.1) Requirement already satisfied: numpy in c:\users\mehak\appdata\local\programs\python\python312\lib\site-packages (from xgboost) (1.26.4) Requirement already satisfied: scipy in c:\users\mehak\appdata\local\programs\python\python312\lib\site-packages (from xgboost) (1.14.0)
In [16]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xgboost as xgb
In [17]:
from sklearn.datasets import load_breast_cancer
Load dataset¶
In [18]:
cancer = load_breast_cancer()
cancer
Out[18]:
{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
1.189e-01],
[2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
8.902e-02],
[1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
8.758e-02],
...,
[1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
7.820e-02],
[2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
1.240e-01],
[7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
7.039e-02]]),
'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),
'frame': None,
'target_names': array(['malignant', 'benign'], dtype='<U9'),
'DESCR': '.. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n:Number of Instances: 569\n\n:Number of Attributes: 30 numeric, predictive attributes and the class\n\n:Attribute Information:\n - radius (mean of distances from center to points on the perimeter)\n - texture (standard deviation of gray-scale values)\n - perimeter\n - area\n - smoothness (local variation in radius lengths)\n - compactness (perimeter^2 / area - 1.0)\n - concavity (severity of concave portions of the contour)\n - concave points (number of concave portions of the contour)\n - symmetry\n - fractal dimension ("coastline approximation" - 1)\n\n The mean, standard error, and "worst" or largest (mean of the three\n worst/largest values) of these features were computed for each image,\n resulting in 30 features. For instance, field 0 is Mean Radius, field\n 10 is Radius SE, field 20 is Worst Radius.\n\n - class:\n - WDBC-Malignant\n - WDBC-Benign\n\n:Summary Statistics:\n\n===================================== ====== ======\n Min Max\n===================================== ====== ======\nradius (mean): 6.981 28.11\ntexture (mean): 9.71 39.28\nperimeter (mean): 43.79 188.5\narea (mean): 143.5 2501.0\nsmoothness (mean): 0.053 0.163\ncompactness (mean): 0.019 0.345\nconcavity (mean): 0.0 0.427\nconcave points (mean): 0.0 0.201\nsymmetry (mean): 0.106 0.304\nfractal dimension (mean): 0.05 0.097\nradius (standard error): 0.112 2.873\ntexture (standard error): 0.36 4.885\nperimeter (standard error): 0.757 21.98\narea (standard error): 6.802 542.2\nsmoothness (standard error): 0.002 0.031\ncompactness (standard error): 0.002 0.135\nconcavity (standard error): 0.0 0.396\nconcave points (standard error): 0.0 0.053\nsymmetry (standard error): 0.008 0.079\nfractal dimension (standard error): 0.001 0.03\nradius (worst): 7.93 36.04\ntexture (worst): 12.02 49.54\nperimeter (worst): 50.41 251.2\narea (worst): 185.2 4254.0\nsmoothness (worst): 0.071 0.223\ncompactness (worst): 0.027 1.058\nconcavity (worst): 0.0 1.252\nconcave points (worst): 0.0 0.291\nsymmetry (worst): 0.156 0.664\nfractal dimension (worst): 0.055 0.208\n===================================== ====== ======\n\n:Missing Attribute Values: None\n\n:Class Distribution: 212 - Malignant, 357 - Benign\n\n:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n:Donor: Nick Street\n\n:Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass. They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree\nConstruction Via Linear Programming." Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree. Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: "Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n.. dropdown:: References\n\n - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction\n for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on\n Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n San Jose, CA, 1993.\n - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and\n prognosis via linear programming. Operations Research, 43(4), pages 570-577,\n July-August 1995.\n - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)\n 163-171.\n',
'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness', 'mean concavity',
'mean concave points', 'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error', 'perimeter error', 'area error',
'smoothness error', 'compactness error', 'concavity error',
'concave points error', 'symmetry error',
'fractal dimension error', 'worst radius', 'worst texture',
'worst perimeter', 'worst area', 'worst smoothness',
'worst compactness', 'worst concavity', 'worst concave points',
'worst symmetry', 'worst fractal dimension'], dtype='<U23'),
'filename': 'breast_cancer.csv',
'data_module': 'sklearn.datasets.data'}
In [19]:
cancer['feature_names']
Out[19]:
array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness', 'mean concavity',
'mean concave points', 'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error', 'perimeter error', 'area error',
'smoothness error', 'compactness error', 'concavity error',
'concave points error', 'symmetry error',
'fractal dimension error', 'worst radius', 'worst texture',
'worst perimeter', 'worst area', 'worst smoothness',
'worst compactness', 'worst concavity', 'worst concave points',
'worst symmetry', 'worst fractal dimension'], dtype='<U23')
In [20]:
cancer['target_names']
Out[20]:
array(['malignant', 'benign'], dtype='<U9')
In [21]:
X = cancer.data
y = cancer.target
In [22]:
xgb_model = xgb.XGBClassifier(objective="binary:logistic",random_state=42)
xgb_model.fit(X,y)
Out[22]:
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=42, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=42, ...)In [23]:
Yp = xgb_model.predict(X)
Yp
Out[23]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])
In [24]:
from sklearn.metrics import confusion_matrix
cm =confusion_matrix(y,Yp)
cm
Out[24]:
array([[212, 0],
[ 0, 357]], dtype=int64)
In [25]:
from sklearn.metrics import accuracy_score
accuracy_score(y,Yp) * 100
Out[25]:
100.0
XGBoost Multi Classification¶
In [26]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
In [27]:
df = pd.read_csv('iris.csv',header=None)
df
Out[27]:
| 0 | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa |
| … | … | … | … | … | … |
| 145 | 6.7 | 3.0 | 5.2 | 2.3 | Iris-virginica |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 | Iris-virginica |
| 147 | 6.5 | 3.0 | 5.2 | 2.0 | Iris-virginica |
| 148 | 6.2 | 3.4 | 5.4 | 2.3 | Iris-virginica |
| 149 | 5.9 | 3.0 | 5.1 | 1.8 | Iris-virginica |
150 rows × 5 columns
In [28]:
X = df.iloc[:,:-1].values
y = df.iloc[:,4].values
In [29]:
from sklearn.preprocessing import LabelEncoder
Ly = LabelEncoder()
y = Ly.fit_transform(y)
y
Out[29]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
In [30]:
Ly.classes_
Out[30]:
array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)
In [31]:
Ly.inverse_transform([2])
Out[31]:
array(['Iris-virginica'], dtype=object)
In [32]:
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=0)
In [33]:
X_train.shape
Out[33]:
(120, 4)
In [34]:
X_test.shape
Out[34]:
(30, 4)
In [35]:
import xgboost as xgb
xgb_model = xgb.XGBClassifier(objective='multi:softprob')
xgb_model.fit(X_train,y_train)
Out[35]:
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, objective='multi:softprob', ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, objective='multi:softprob', ...)In [36]:
X_test[0]
Out[36]:
array([5.8, 2.8, 5.1, 2.4])
In [37]:
y_test[0]
Out[37]:
2
In [38]:
Yp = xgb_model.predict(X_test)
Yp
Out[38]:
array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1,
0, 0, 2, 0, 0, 1, 1, 0], dtype=int64)
In [39]:
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,Yp)
cm
Out[39]:
array([[11, 0, 0],
[ 0, 13, 0],
[ 0, 0, 6]], dtype=int64)
In [40]:
from sklearn.metrics import accuracy_score
accuracy_score(y_test,Yp) * 100
Out[40]:
100.0
XGBoost Regression¶
In [41]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [44]:
df = pd.read_csv('advertising.csv')
df
Out[44]:
| TV | Radio | Newspaper | Sales | |
|---|---|---|---|---|
| 0 | 230.1 | 37.8 | 69.2 | 22.1 |
| 1 | 44.5 | 39.3 | 45.1 | 10.4 |
| 2 | 17.2 | 45.9 | 69.3 | 12.0 |
| 3 | 151.5 | 41.3 | 58.5 | 16.5 |
| 4 | 180.8 | 10.8 | 58.4 | 17.9 |
| … | … | … | … | … |
| 195 | 38.2 | 3.7 | 13.8 | 7.6 |
| 196 | 94.2 | 4.9 | 8.1 | 14.0 |
| 197 | 177.0 | 9.3 | 6.4 | 14.8 |
| 198 | 283.6 | 42.0 | 66.2 | 25.5 |
| 199 | 232.1 | 8.6 | 8.7 | 18.4 |
200 rows × 4 columns
In [45]:
df.shape
Out[45]:
(200, 4)
In [46]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 200 entries, 0 to 199 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TV 200 non-null float64 1 Radio 200 non-null float64 2 Newspaper 200 non-null float64 3 Sales 200 non-null float64 dtypes: float64(4) memory usage: 6.4 KB
In [47]:
df.isnull().sum()
Out[47]:
TV 0 Radio 0 Newspaper 0 Sales 0 dtype: int64
In [48]:
df.describe()
Out[48]:
| TV | Radio | Newspaper | Sales | |
|---|---|---|---|---|
| count | 200.000000 | 200.000000 | 200.000000 | 200.000000 |
| mean | 147.042500 | 23.264000 | 30.554000 | 15.130500 |
| std | 85.854236 | 14.846809 | 21.778621 | 5.283892 |
| min | 0.700000 | 0.000000 | 0.300000 | 1.600000 |
| 25% | 74.375000 | 9.975000 | 12.750000 | 11.000000 |
| 50% | 149.750000 | 22.900000 | 25.750000 | 16.000000 |
| 75% | 218.825000 | 36.525000 | 45.100000 | 19.050000 |
| max | 296.400000 | 49.600000 | 114.000000 | 27.000000 |
In [49]:
sns.pairplot(df)
plt.show()
In [50]:
df.corr()
Out[50]:
| TV | Radio | Newspaper | Sales | |
|---|---|---|---|---|
| TV | 1.000000 | 0.054809 | 0.056648 | 0.901208 |
| Radio | 0.054809 | 1.000000 | 0.354104 | 0.349631 |
| Newspaper | 0.056648 | 0.354104 | 1.000000 | 0.157960 |
| Sales | 0.901208 | 0.349631 | 0.157960 | 1.000000 |
In [51]:
X = df.iloc[:,0:1].values
y = df.iloc[:,3].values
In [52]:
plt.scatter(X,y)
plt.show()
In [53]:
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,train_size=0.8,random_state=42)
In [54]:
import xgboost as xgb
xgb_model = xgb.XGBRegressor(objective="reg:linear")
xgb_model.fit(X_train,y_train)
C:\Users\Mehak\AppData\Local\Programs\Python\Python312\Lib\site-packages\xgboost\core.py:158: UserWarning: [13:55:44] WARNING: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\xgboost\xgboost-ci-windows\src\objective\regression_obj.cu:227: reg:linear is now deprecated in favor of reg:squarederror. warnings.warn(smsg, UserWarning)
Out[54]:
XGBRegressor(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, objective='reg:linear', ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
XGBRegressor(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, objective='reg:linear', ...)In [55]:
import xgboost as xgb
xgb_model = xgb.XGBRegressor(objective="reg:squarederror")
xgb_model.fit(X_train,y_train)
Out[55]:
XGBRegressor(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
XGBRegressor(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=None, ...)In [56]:
Yp = xgb_model.predict(X_test)
Yp
Out[56]:
array([15.624145 , 19.189974 , 17.950972 , 7.062304 , 19.618546 ,
14.399897 , 22.245396 , 10.13141 , 16.78554 , 16.419771 ,
7.503492 , 10.13141 , 18.4142 , 3.2166266, 12.266199 ,
16.701488 , 3.2166266, 17.079662 , 14.399897 , 17.722656 ,
20.570726 , 10.019968 , 10.578899 , 18.932234 , 13.343296 ,
10.13141 , 17.18644 , 12.266199 , 13.004839 , 6.525969 ,
16.653044 , 13.343296 , 17.079662 , 5.623542 , 20.080675 ,
17.722656 , 10.13141 , 25.038157 , 11.321146 , 8.794699 ],
dtype=float32)
In [57]:
xgb_model.score(X_train,y_train) * 100
Out[57]:
98.98101371613015
In [58]:
xgb_model.score(X_test,y_test) * 100
Out[58]:
79.06310305325196
In [59]:
plt.plot(y_test,label='Original')
plt.plot(Yp,label='Predicted')
plt.legend()
plt.show()
In [60]:
xgb_model.predict(np.array([[566]]))
Out[60]:
array([23.706078], dtype=float32)
In [61]:
X_test
Out[61]:
array([[163.3],
[195.4],
[292.9],
[ 11.7],
[220.3],
[ 75.1],
[216.8],
[ 50. ],
[222.4],
[175.1],
[ 31.5],
[ 56.2],
[234.5],
[ 5.4],
[139.5],
[170.2],
[ 7.3],
[197.6],
[ 75.3],
[237.4],
[229.5],
[ 67.8],
[ 38. ],
[250.9],
[ 69. ],
[ 53.5],
[213.5],
[139.3],
[ 87.2],
[ 8.4],
[199.8],
[ 69.2],
[198.9],
[ 16.9],
[280.7],
[238.2],
[ 48.3],
[273.7],
[117.2],
[ 27.5]])
