# Ensemble Learning | Machine Learning in Python | KGP Talkie

## What is Ensemble Learning?

We can define Ensemble Learning in this way it uses multiple machine learning models or multiple set of models for the same algorithm which try to make a better prediction.

`Ensemble Learning`

model works by training different models on the same dataset and makes prediction iindividually and once the prediction is made then these results are combines with some statistical methods to get final prediction

In one sentence we can explain like this there is a dataset where `multiple algorithms`

are trained on the same dataset and then finally `predictions`

are made based on the outcomes of the individual `machine learning`

algorithms.

Let me explain this with an example of `cricket team`

, in cricket team or any other team every `few players`

are specialized in `some fields`

(batting, fast bowling, fielding, keeping, … etc). In the same way every `algorithm`

has its own `feature set`

. There are `multiple algorithms`

and they are specialized in some way so once we combine all of these algorithms it’s easy to get the final `predictions`

.

### Why Ensemble Learning

Now we will try to understand why we use Ensemble learning:

If we try to start with a simple model to achive `high accuracy`

by using single algorithm it might be endup with `overfitting`

or `underfitting`

.

Every model has its own `strength`

and `weakness`

. If we combine multiple models it will help us to hide weakness of individual models sothat we can cover weakness of others.

It creates some errors, The `error`

emerging from any machine model can be broken down into `three`

components mathematically. Following are these component:

- Bias
- Variance
- Irreducible error

To understand these errors have a look at the following figure:

**Bias error** is useful to quantify how much on an `average`

are the predicted values different from the actual value.

**Variance** on the other side quantifies how are the prediction made on the `same observation`

different from each other.

Now we will try to understand `bias - variance`

trade off from the following figure.

By increasing `model complexity`

, total error will `decrease`

till some point and then it will start to `increase`

. W need to select `optimum model complexity`

to get less error.

For low complexity model : high bias and low variance

For high complexity model : low bias and high variance

If you are getting high bias then you have a fair chance to increase `model complexity`

. And otherside it you are getting `high variance`

, you need to decrease `model complexity`

that’s how any machine learning algorithm works.

Let’s look into the types of Ensemble Learning:

### Type of Ensemble Learning

- Basic Ensemble Techniques
- Max Voting
- Averaging
- Weighted Average

- Advanced Ensemble Techniques
- Stacking
- Blending
- Bagging
- Boosting

- Algorithms based on Bagging
- Bagging meta-estimator
- Random Forest

- Boosting Algorithms
- AdaBoost
- GBM
- XGB
- Light GBM
- CatBoost

**Max Voting**

The `max voting`

method is generally used for classification problems. In this technique, multiple models are used to make `predictions`

for each data point.

**Averaging**

Similar to the `max voting`

technique, multiple predictions are made for each data point in averaging.

**Weighted Average**

This is an extension of the `averaging`

method. All models are assigned different weights defining the importance of each model for prediction.

#### Bagging

`Bagging`

is also known as `Bootstrapping`

. It is a `sampling`

technique in which we create subsets of observations from the original dataset, with replacement. The size of the subsets is the same as the size of the original set.

- combining predictions that belong to the same type.
- Aim to decrease variance, not bias.
- Different training data subsets are randomly drawn with replacement from the entire training dataset.

To explain `bagging`

Random Forest(below figure) is the best example.

It creates `multiple subsets`

like `decision tree`

and it makes a `prediction`

for each decision tree then if random forest is `classifier`

it will take `max voting`

otherwise if it is a `regressor`

it will take `avearge`

from each of these subset of the trees .

#### Boosting

`Boosting`

is a sequential process, where each subsequent model attempts to correct the `errors`

of the previous model. The succeeding models are dependent on the previous model.

Letâ€™s understand the way boosting works in the below steps.

- Combining predictions that belong to the different types.
- Aim to decrease bias, not variance.
- Models are weighted according to their performance.

Let’s now understand `boosting`

from the following figure: At first we have our `original dataset`

,our first algorithm creates a `plane`

there for that we have SVM classifier, Random Forest classifier, etc and it found out that there are some `errors`

in the plane . To rectife that `errors`

, we will train other model and after this again we will train other model which identifies `errors`

.

Finally, we combine all three models together which perfectly `classify`

our original dataset.

## Algorithms Implimentation in sklearn

- Bagging
- Random Forest

- Boosting
- XGBosst
- AdaBoost
- Gradient Boosting

**Random Forest** is another ensemble machine learning algorithm that follows the bagging technique

**XGBoost (extreme Gradient Boosting)** is an advanced implementation of the gradient boosting algorithm

**Adaptive boosting or AdaBoost** is one of the simplest boosting algorithms

**Gradient Boosting or GBM** is another ensemble machine learning algorithm that works for both regression and classification problems

## Data Preparation

Importing required libraries

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import numpy as np

Loading the training data set

from sklearn import datasets, metrics from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler

cancer = datasets.load_breast_cancer()

Let’s go ahead and get the description the breast cancer data set.

print(cancer.DESCR)

.. _breast_cancer_dataset: Breast cancer wisconsin (diagnostic) dataset -------------------------------------------- **Data Set Characteristics:** :Number of Instances: 569 :Number of Attributes: 30 numeric, predictive attributes and the class :Attribute Information: - radius (mean of distances from center to points on the perimeter) - texture (standard deviation of gray-scale values) - perimeter - area - smoothness (local variation in radius lengths) - compactness (perimeter^2 / area - 1.0) - concavity (severity of concave portions of the contour) - concave points (number of concave portions of the contour) - symmetry - fractal dimension ("coastline approximation" - 1) The mean, standard error, and "worst" or largest (mean of the three worst/largest values) of these features were computed for each image, resulting in 30 features. For instance, field 0 is Mean Radius, field 10 is Radius SE, field 20 is Worst Radius. - class: - WDBC-Malignant - WDBC-Benign :Summary Statistics: ===================================== ====== ====== Min Max ===================================== ====== ====== radius (mean): 6.981 28.11 texture (mean): 9.71 39.28 perimeter (mean): 43.79 188.5 area (mean): 143.5 2501.0 smoothness (mean): 0.053 0.163 compactness (mean): 0.019 0.345 concavity (mean): 0.0 0.427 concave points (mean): 0.0 0.201 symmetry (mean): 0.106 0.304 fractal dimension (mean): 0.05 0.097 radius (standard error): 0.112 2.873 texture (standard error): 0.36 4.885 perimeter (standard error): 0.757 21.98 area (standard error): 6.802 542.2 smoothness (standard error): 0.002 0.031 compactness (standard error): 0.002 0.135 concavity (standard error): 0.0 0.396 concave points (standard error): 0.0 0.053 symmetry (standard error): 0.008 0.079 fractal dimension (standard error): 0.001 0.03 radius (worst): 7.93 36.04 texture (worst): 12.02 49.54 perimeter (worst): 50.41 251.2 area (worst): 185.2 4254.0 smoothness (worst): 0.071 0.223 compactness (worst): 0.027 1.058 concavity (worst): 0.0 1.252 concave points (worst): 0.0 0.291 symmetry (worst): 0.156 0.664 fractal dimension (worst): 0.055 0.208 ===================================== ====== ====== :Missing Attribute Values: None :Class Distribution: 212 - Malignant, 357 - Benign :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian :Donor: Nick Street :Date: November, 1995 This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2 Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes. The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

X = cancer.data y = cancer.target

Let’s check the shape of X and Y :

X.shape, y.shape

((569, 30), (569,))

If we see here scale of the each feature is different that is dome features are in the range `10s`

some are in `100s`

. It is better to `standardize`

our data for better visualization.

scaler = StandardScaler() X_scaled = scaler.fit_transform(X) X_scaled[: 2]

array([[ 1.09706398e+00, -2.07333501e+00, 1.26993369e+00, 9.84374905e-01, 1.56846633e+00, 3.28351467e+00, 2.65287398e+00, 2.53247522e+00, 2.21751501e+00, 2.25574689e+00, 2.48973393e+00, -5.65265059e-01, 2.83303087e+00, 2.48757756e+00, -2.14001647e-01, 1.31686157e+00, 7.24026158e-01, 6.60819941e-01, 1.14875667e+00, 9.07083081e-01, 1.88668963e+00, -1.35929347e+00, 2.30360062e+00, 2.00123749e+00, 1.30768627e+00, 2.61666502e+00, 2.10952635e+00, 2.29607613e+00, 2.75062224e+00, 1.93701461e+00], [ 1.82982061e+00, -3.53632408e-01, 1.68595471e+00, 1.90870825e+00, -8.26962447e-01, -4.87071673e-01, -2.38458552e-02, 5.48144156e-01, 1.39236330e-03, -8.68652457e-01, 4.99254601e-01, -8.76243603e-01, 2.63326966e-01, 7.42401948e-01, -6.05350847e-01, -6.92926270e-01, -4.40780058e-01, 2.60162067e-01, -8.05450380e-01, -9.94437403e-02, 1.80592744e+00, -3.69203222e-01, 1.53512599e+00, 1.89048899e+00, -3.75611957e-01, -4.30444219e-01, -1.46748968e-01, 1.08708430e+00, -2.43889668e-01, 2.81189987e-01]])

from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier import xgboost as xgb

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.2, random_state = 1, stratify = y)

### RandomForestClassifier()

A random forest is a `estimator`

that fits a number of `decision tree classifiers`

on various sub-samples of the dataset and uses averaging to improve the predictive `accuracy`

and control `over-fitting`

.

### AdaBoostClassifier()

It is a `estimator`

that begins by fitting a classifier on the original dataset and then fits additional copies of the `classifier`

on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent `classifiers`

focus more on difficult cases.

### GradientBoostingClassifier()

It builds an `additive`

model in a forward stage-wise fashion; it allows for the `optimization`

of arbitrary differentiable `loss functions`

.

### XGBClassifier()

`Data Matrix`

is a internal data structure that used by `XGBoost`

which is optimized for both `memory`

efficiency and `training`

speed. You can construct `DMatrix`

from multiple different sources of data.

Let’s have a look at the following code:

rfc = RandomForestClassifier(n_estimators=200, random_state=1) abc = AdaBoostClassifier(n_estimators=200, random_state= 1, learning_rate=0.01) gbc = GradientBoostingClassifier(n_estimators=200, random_state=1, learning_rate=0.01) xgb_clf = xgb.XGBClassifier(n_estimators=200, learning_rate=0.01, random_state=1)

rfc.fit(X_train, y_train) abc.fit(X_train, y_train) gbc.fit(X_train, y_train) xgb_clf.fit(X_train, y_train)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1, importance_type='gain', interaction_constraints='', learning_rate=0.01, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=200, n_jobs=0, num_parallel_tree=1, random_state=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', validate_parameters=1, verbosity=None)

Let’s print the accuracies of Random Forest, AdaBoost, Gradient Boost, XGBoost.

print('Random Forest: ', rfc.score(X_test, y_test)) print('AdaBoost: ', abc.score(X_test, y_test)) print('Gradient Boost: ', gbc.score(X_test, y_test)) print('XGBoost: ', xgb_clf.score(X_test, y_test))

Random Forest: 0.9473684210526315 AdaBoost: 0.9473684210526315 Gradient Boost: 0.9736842105263158 XGBoost: 0.9649122807017544

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