# Praktikum 2

**Download Dataset Terlebih Dahulu** :arrow\_down:

{% file src="/files/UDleP5lvfnmmZNcJr8xr" %}

### Boosting dengan AdaBoost

Pada kasus ini kita akan menggunakan salah satu metode boosting yaitu AdaBoost untuk mengklasifikasikan jenis bunga Iris. Dalam latihan ini kita akan menggunakan dataset Iris yang sangat lazim digunakan. Latihan ini akan melakukan prediksi memprediksi 3 jenis bunga Iris yaitu, Iris Setosa, Iris Versicolor, dan Iris Virginica berdasarkan panjang dan lebar sepal dan petal.

Kita akan membandingkan performa dari algoritma Decision Tree dan AdaBoost pada kasus ini.

#### Import Library

```python
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier # import DT
from sklearn.ensemble import AdaBoostClassifier # import AdaBoost
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from sklearn.preprocessing import LabelEncoder # Kebutuhan encoding label
```

#### Persiapan Data

```python
# Load data
df = pd.read_csv('data/iris.csv')

df.head()
```

```
   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0   1            5.1           3.5            1.4           0.2  Iris-setosa
1   2            4.9           3.0            1.4           0.2  Iris-setosa
2   3            4.7           3.2            1.3           0.2  Iris-setosa
3   4            4.6           3.1            1.5           0.2  Iris-setosa
4   5            5.0           3.6            1.4           0.2  Iris-setosa
```

```python
# Cek kolom null
df.isnull().sum()
```

```
Id               0
SepalLengthCm    0
SepalWidthCm     0
PetalLengthCm    0
PetalWidthCm     0
Species          0
dtype: int64
```

```python
# Seleksi fitur
X = df.iloc[:,2:-1]
y = df['Species']

# encode label
ec = LabelEncoder()
y = ec.fit_transform(y)

# Cek jumlah fitur dan instance
print(X.shape)

# Cek label
print(y)
```

```
(150, 3)
[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]
```

#### Split data training dan testing

```python
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=1)
```

#### Training Decision Tree

```python
# Secara default, DecisionTreeClassifier dari scikit-learn akan menggunakan nilai "Gini" untuk kriteria
# Terdapat beberapa "hyperparamater" yang dapat digunakan. Silahka baca dokumentasi
# Pada kasus ini kita akan menggunakan parameter default
dt = DecisionTreeClassifier()

# Sesuaikan dt ke set training
dt.fit(X_train, y_train)

# Memprediksi label set test
y_pred_dt = dt.predict(X_test)

#  menghitung set accuracy
acc_dt = accuracy_score(y_test, y_pred_dt)
print("Test set accuracy: {:.2f}".format(acc_dt))
print(f"Test set accuracy: {acc_dt}")
```

```
Test set accuracy: 0.97
Test set accuracy: 0.9666666666666667
```

#### Training AdaBoost

```python
# Pada kasus kali ini kita akan menggunakan estimator pada AdaBoost
# Untuk detail parameter (hyperparameter) silahkan cek dokumentasi

ada = AdaBoostClassifier(n_estimators=2)

# Sesuaikan dt ke set training
ada.fit(X_train, y_train)

# Memprediksi label set test
y_pred_ada = ada.predict(X_test)

#  menghitung set accuracy
acc_ada = accuracy_score(y_test, y_pred_ada)
print("Test set accuracy: {:.2f}".format(acc_ada))
print(f"Test set accuracy: {acc_ada}")
```

```
Test set accuracy: 0.97
Test set accuracy: 0.9666666666666667
```

```python
```


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