RandomForest for Iris classification

In [1]:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn import datasets
from sklearn import metrics

import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from sklearn import datasets from sklearn import metrics

In [2]:

iris = datasets.load_iris()

iris = datasets.load_iris()

In [3]:

data=pd.DataFrame({
    'sepal_length':iris.data[:,0],
    'sepal_width':iris.data[:,1],
    'petal_length':iris.data[:,2],
    'petal_width':iris.data[:,3],
    'species':iris.target
})
data.head()

data=pd.DataFrame({ 'sepal_length':iris.data[:,0], 'sepal_width':iris.data[:,1], 'petal_length':iris.data[:,2], 'petal_width':iris.data[:,3], 'species':iris.target }) data.head()

Out[3]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	0
1	4.9	3.0	1.4	0.2	0
2	4.7	3.2	1.3	0.2	0
3	4.6	3.1	1.5	0.2	0
4	5.0	3.6	1.4	0.2	0

In [4]:

X=data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] 
y=data['species'] 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

X=data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] y=data['species'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

In [5]:

from sklearn.ensemble import RandomForestClassifier
clf=RandomForestClassifier(n_estimators=100)
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)

from sklearn.ensemble import RandomForestClassifier clf=RandomForestClassifier(n_estimators=100) clf.fit(X_train,y_train) y_pred=clf.predict(X_test)

In [6]:

from sklearn import metrics
print("Accuracy :",metrics.accuracy_score(y_test, y_pred))

from sklearn import metrics print("Accuracy :",metrics.accuracy_score(y_test, y_pred))

Accuracy : 0.9555555555555556

In [7]:

clf

clf

Out[7]:

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)

In [8]:

feature_imp = pd.Series(clf.feature_importances_,index=iris.feature_names).sort_values(ascending=False)
feature_imp

feature_imp = pd.Series(clf.feature_importances_,index=iris.feature_names).sort_values(ascending=False) feature_imp

Out[8]:

petal width (cm)     0.501632
petal length (cm)    0.379573
sepal length (cm)    0.096711
sepal width (cm)     0.022084
dtype: float64

In [9]:

sns.barplot(x=feature_imp, y=feature_imp.index)
plt.xlabel('Feature Importance Score')
plt.ylabel('Features')
plt.title("Visualizing Important Features")
#plt.legend()
plt.show()

sns.barplot(x=feature_imp, y=feature_imp.index) plt.xlabel('Feature Importance Score') plt.ylabel('Features') plt.title("Visualizing Important Features") #plt.legend() plt.show()

In [10]:

X=data[['petal_length', 'petal_width']] 
y=data['species']                                       
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.70, random_state=5)

X=data[['petal_length', 'petal_width']] y=data['species'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.70, random_state=5)

In [11]:

clf_sf=RandomForestClassifier(n_estimators=100)
clf_sf.fit(X_train,y_train)
y_pred=clf_sf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

clf_sf=RandomForestClassifier(n_estimators=100) clf_sf.fit(X_train,y_train) y_pred=clf_sf.predict(X_test) print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Accuracy: 0.9523809523809523

In [ ]: