- Machine Learning with Python
- Home
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- Understanding Data with Statistics
- Understanding Data with Visualization
- Preparing Data
- Data Feature Selection
- ML Algorithms − Classification
- Introduction
- Logistic Regression
- Support Vector Machine(SVM)
- Decision Tree
- Naïve Bayes
- Random Forest
- ML Algorithms − Regression
- Overview
- Linear Regression
- ML Algorithms − Clustering
- Overview
- K-Means Algorithm
- Mean Shift Algorithm
- Hierarchical Clustering
- ML Algorithms − KNN Algorithm
- Finding Nearest Neighbors
- Performance Metrics
- Automatic Workflows
- Improving Performance of ML Models
- Improving Performance of ML Model(contd..)

- Useful Resources
- Quick Guide
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- Discussion

It is one the most successful boosting ensemble algorithm. The main key of this algorithm is in the way they give weights to the instances in dataset. Due to this the algorithm needs to pay less attention to the instances while constructing subsequent models.

In the following Python recipe, we are going to build Ada Boost ensemble model for classification by using *AdaBoostClassifier* class of *sklearn* on Pima Indians diabetes dataset.

First, import the required packages as follows −

from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.ensemble import AdaBoostClassifier

Now, we need to load the Pima diabetes dataset as did in previous examples −

path = r"C:\pima-indians-diabetes.csv" headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = read_csv(path, names = headernames) array = data.values X = array[:,0:8] Y = array[:,8]

Next, give the input for 10-fold cross validation as follows −

seed = 5 kfold = KFold(n_splits = 10, random_state = seed)

We need to provide the number of trees we are going to build. Here we are building 150 trees with split points chosen from 5 features −

num_trees = 50

Next, build the model with the help of following script −

model = AdaBoostClassifier(n_estimators = num_trees, random_state = seed)

Calculate and print the result as follows −

results = cross_val_score(model, X, Y, cv = kfold) print(results.mean())

**Output**

0.7539473684210527

The output above shows that we got around 75% accuracy of our AdaBoost classifier ensemble model.

machine_learning_with_python_improving_performance_of_ml_models.htm

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