machinelearning_practices/decisionTree.py at main · seydanur-hemithea/machinelearning_practices · GitHub

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from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier,plot_tree
from sklearn.metrics import accuracy_score,confusion_matrix
import matplotlib.pyplot as plt

iris=load_iris()

X=iris.data
y=iris.target
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
tree_cls= DecisionTreeClassifier(criterion="gini",max_depth=4,random_state=42)
tree_cls.fit(X_train,y_train)
y_pred=tree_cls.predict(X_test)
acc=accuracy_score(y_test,y_pred)
cm=confusion_matrix(y_test, y_pred)
print(acc)
print(cm)
plt.figure(figsize=(15,10))
plot_tree(tree_cls,filled=True,feature_names=iris.feature_names,class_names=list(iris.target_names))
plt.show()

feature_importance=tree_cls.feature_importances_
feature_names=iris.feature_names
feature_importance_sorted=sorted(zip(feature_importance,feature_names),reverse=True)
for importance,feature_names in feature_importance_sorted:
 print(f"{feature_names}:{importance}")
#%%
from sklearn.datasets import load_iris


from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.tree import DecisionTreeRegressor

import matplotlib.pyplot as plt
import numpy as np
n_classes=len(iris.target_names)
plot_colors="ryb"
iris=load_iris()
for pairidx,pair in enumerate([[0,1],[0,2],[0,3],[1,2],[1,3],[2,3]]):
    X=iris.data[:,pair]
    y=iris.target

    clf=DecisionTreeClassifier().fit(X,y)
    ax=plt.subplot(2,3,pairidx+1)
    plt.tight_layout(h_pad=0.5,w_pad=0.5,pad=2.5)
    DecisionBoundaryDisplay.from_estimator(clf,
                                           X,
                                           cmap=plt.cm.RdYlBu,
                                           response_method="predict",
                                           ax=ax,
                                           xlabel=iris.feature_names[pair[0]]
                                           ,ylabel=iris.feature_names[pair[1]])
    for i,color in zip(range(n_classes),plot_colors):
        idx=np.where(y==1)
        plt.scatter(X[idx,0],X[idx,1],c=color,label=iris.target_names[i],
                    cmap=plt.cm.RdYlBu,
                    edgecolors="black")
plt.legend()

#%%
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
import numpy as np
diabetes=load_diabetes()


X=diabetes.data
y=diabetes.target

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
tree_reg= DecisionTreeRegressor(random_state=42)
tree_reg.fit(X_train,y_train)

y_pred=tree_reg.predict(X_test)

mse=mean_squared_error(y_test,y_pred)
print("mse:",mse)

rmse=np.sqrt(mse)
print("rmse:",rmse)

#%%
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import matplotlib.pyplot as plt
X=np.sort(5*np.random.rand(80,1),axis=0)
y=np.sin(X).ravel()
y[::5]+=0.5*(0.5 - np.random.rand(16))
regr_1=DecisionTreeRegressor(max_depth=2)
regr_2=DecisionTreeRegressor(max_depth=5)
regr_1.fit(X,y)
regr_2.fit(X,y)
X_test=np.arange(0,5,0.05)[:,np.newaxis]
y_pred1=regr_1.predict(X_test)
y_pred2=regr_2.predict(X_test)

plt.figure()
plt.scatter(X,y,c="red",label="data")
plt.plot(X_test,y_pred1,color="blue",label="max depth=2",linewidth=2)
plt.plot(X_test,y_pred2,color="green",label="max depth=5",linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.legend()