A.I
Explolation2 아이리스 분류하기 본문
1. 아이리스 분류하기¶
In [1]:
from sklearn.datasets import load_iris
iris = load_iris()
print(type(dir(iris)))
# dir()는 객체가 어떤 변수와 메서드를 가지고 있는지 나열함
<class 'list'>
1-1. 아이리스 데이터 내부 구조 파악
In [2]:
iris.keys()
Out[2]:
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])
In [3]:
iris_data = iris.data
print(iris_data.shape)
#shape는 배열의 형상정보를 출력
(150, 4)
In [4]:
iris_data[0]
# 예시로 하나만 출력
Out[4]:
array([5.1, 3.5, 1.4, 0.2])
순서대로 sepal length, sepal width, petal length, petal width 를 나타냄
In [5]:
iris_label = iris.target
print(iris_label.shape)
iris_label
(150,)
Out[5]:
array([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])
In [6]:
iris.target_names
Out[6]:
array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
150개의 배열값으로 추측해보면 어떤 꽃인지 추측가능
In [7]:
iris.filename
# 아이리스 데이터셋의 저장경로
Out[7]:
'/home/ssac24/anaconda3/envs/aiffel/lib/python3.7/site-packages/sklearn/datasets/data/iris.csv'
1-2. panda를 이용한 머신러닝¶
In [8]:
import pandas as pd
print(pd.__version__)
1.1.5
In [10]:
iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)
iris_df["label"] = iris.target
iris_df
Out[10]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | label | |
|---|---|---|---|---|---|
| 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 |
| ... | ... | ... | ... | ... | ... |
| 145 | 6.7 | 3.0 | 5.2 | 2.3 | 2 |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 | 2 |
| 147 | 6.5 | 3.0 | 5.2 | 2.0 | 2 |
| 148 | 6.2 | 3.4 | 5.4 | 2.3 | 2 |
| 149 | 5.9 | 3.0 | 5.1 | 1.8 | 2 |
150 rows × 5 columns
문제지 : 머신러닝 모델에게 입력되는 데이터. feature라고 부르기도 한다. 변수 이름으로는 X를 많이 사용한다. 앞의 4가지 cm 데이터를 말함
정답지 : 머신러닝 모델이 맞추어야 하는 데이터. label, 또는 target이라고 부르기도 한다. 변수 이름으로는 y를 많이 사용한다.
1-3. 데이터셋 분리¶
In [13]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris_data,
iris_label,
test_size=0.2,
random_state=7)
print('X_train 개수: ', len(X_train), ', X_test 개수: ', len(X_test))
# 3번째 인자는 전체 데이터의 양에서 몇 퍼센트를 테스트셋으로 나눌지 구분하는 인자
# random_state는 random_seed로 대체가능
X_train 개수: 120 , X_test 개수: 30
1-4. 의사결정트리 사용해보기¶
In [15]:
from sklearn.tree import DecisionTreeClassifier
decision_tree = DecisionTreeClassifier(random_state=32)
print(decision_tree._estimator_type)
classifier
In [18]:
decision_tree.fit(X_train, y_train)
# fit = 학습시킨다
Out[18]:
DecisionTreeClassifier(random_state=32)
1-5. 테스트¶
In [20]:
y_pred = decision_tree.predict(X_test)
y_pred
Out[20]:
array([2, 1, 0, 1, 2, 0, 1, 1, 0, 1, 2, 1, 0, 2, 0, 2, 2, 2, 0, 0, 1, 2,
1, 1, 2, 2, 1, 1, 2, 2])
In [21]:
y_test
Out[21]:
array([2, 1, 0, 1, 2, 0, 1, 1, 0, 1, 1, 1, 0, 2, 0, 1, 2, 2, 0, 0, 1, 2,
1, 2, 2, 2, 1, 1, 2, 2])
In [22]:
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
accuracy
# 정확도를 측정할수 있는 매트릭스
Out[22]:
0.9
요약¶
In [24]:
# (1) 필요한 모듈 import
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
# (2) 데이터 준비
iris = load_iris()
iris_data = iris.data
iris_label = iris.target
# (3) train, test 데이터 분리
X_train, X_test, y_train, y_test = train_test_split(iris_data,
iris_label,
test_size=0.2,
random_state=7)
# (4) 모델 학습 및 예측
decision_tree = DecisionTreeClassifier(random_state=32)
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_test)
print(classification_report(y_test, y_pred))
precision recall f1-score support
0 1.00 1.00 1.00 7
1 0.91 0.83 0.87 12
2 0.83 0.91 0.87 11
accuracy 0.90 30
macro avg 0.91 0.91 0.91 30
weighted avg 0.90 0.90 0.90 30
1-6 랜덤포레스트¶
In [26]:
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(iris_data,
iris_label,
test_size=0.2,
random_state=25)
random_forest = RandomForestClassifier(random_state=32)
random_forest.fit(X_train, y_train)
y_pred = random_forest.predict(X_test)
print(classification_report(y_test, y_pred))
precision recall f1-score support
0 1.00 1.00 1.00 9
1 0.92 0.92 0.92 13
2 0.88 0.88 0.88 8
accuracy 0.93 30
macro avg 0.93 0.93 0.93 30
weighted avg 0.93 0.93 0.93 30
내장 분류 모델들¶
- SVM(서포트 벡터 머신) https://excelsior-cjh.tistory.com/66?category=918734
In [28]:
from sklearn import svm
svm_model = svm.SVC()
print(svm_model._estimator_type)
svm_model.fit(X_train, y_train)
y_pred = svm_model.predict(X_test)
print(classification_report(y_test, y_pred))
classifier
precision recall f1-score support
0 1.00 1.00 1.00 9
1 1.00 0.92 0.96 13
2 0.89 1.00 0.94 8
accuracy 0.97 30
macro avg 0.96 0.97 0.97 30
weighted avg 0.97 0.97 0.97 30
- Stochastic Gradient Descent Classifier (SGDClassifier) - 확률적 경사하강법 https://scikit-learn.org/stable/modules/sgd.html
In [30]:
from sklearn.linear_model import SGDClassifier
sgd_model = SGDClassifier()
print(sgd_model._estimator_type)
sgd_model.fit(X_train, y_train)
y_pred = sgd_model.predict(X_test)
print(classification_report(y_test, y_pred))
classifier
precision recall f1-score support
0 1.00 0.89 0.94 9
1 0.85 0.85 0.85 13
2 0.78 0.88 0.82 8
accuracy 0.87 30
macro avg 0.87 0.87 0.87 30
weighted avg 0.87 0.87 0.87 30
- Logistic Regression - 로지스틱 회귀 http://hleecaster.com/ml-logistic-regression-concept/
In [33]:
from sklearn.linear_model import LogisticRegression
logistic_model = LogisticRegression(max_iter=200)
# 로지스틱의 max_iter값은 기본으로 100으로 되어있으므로 그 수를 늘려 메모리에 저장되는 벡터수를 늘려주면 된다.
print(logistic_model._estimator_type)
logistic_model.fit(X_train, y_train)
y_pred = logistic_model.predict(X_test)
print(classification_report(y_test, y_pred))
classifier
precision recall f1-score support
0 1.00 1.00 1.00 9
1 1.00 0.92 0.96 13
2 0.89 1.00 0.94 8
accuracy 0.97 30
macro avg 0.96 0.97 0.97 30
weighted avg 0.97 0.97 0.97 30
2. 정확도 측정에 대해 알아보자¶
In [35]:
from sklearn.datasets import load_digits
digits = load_digits()
# 손글씨 데이터 가져오기
digits.keys()
Out[35]:
dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])
In [36]:
# 데이터 개수
digits_data = digits.data
# 데이터 유형
digits_data.shape
Out[36]:
(1797, 64)
In [37]:
digits_data[0]
# 데이터가 8X8픽셀로 이루어진 배열임을 알 수 있다.
Out[37]:
array([ 0., 0., 5., 13., 9., 1., 0., 0., 0., 0., 13., 15., 10.,
15., 5., 0., 0., 3., 15., 2., 0., 11., 8., 0., 0., 4.,
12., 0., 0., 8., 8., 0., 0., 5., 8., 0., 0., 9., 8.,
0., 0., 4., 11., 0., 1., 12., 7., 0., 0., 2., 14., 5.,
10., 12., 0., 0., 0., 0., 6., 13., 10., 0., 0., 0.])
In [38]:
import matplotlib.pyplot as plt
# 이미지를 보기 위한 라이브러리
%matplotlib inline
plt.imshow(digits.data[0].reshape(8, 8), cmap='gray')
plt.axis('off')
plt.show()
In [40]:
# 여러개 보기
for i in range(10):
plt.subplot(2, 5, i+1)
plt.imshow(digits.data[i].reshape(8, 8), cmap='gray')
plt.axis('off')
plt.show()
# target 데이터 구성
digits_label = digits.target
print(digits_label.shape)
digits_label[:20]
(1797,)
Out[40]:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [41]:
# 3일때만 표기
new_label = [3 if i == 3 else 0 for i in digits_label]
new_label[:20]
Out[41]:
[0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0]
2-1. 손글씨 데이터 의사결정트리로 정확도 측정¶
In [43]:
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
digits = load_digits()
digits_data = digits.data
digits_label = digits.target
# (3) train, test 데이터 분리
X_train, X_test, y_train, y_test = train_test_split(digits_data,
new_label,
test_size=0.2,
random_state=20)
# (4) 모델 학습 및 예측
decision_tree = DecisionTreeClassifier(random_state=20)
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_test)
print(classification_report(y_test, y_pred))
accuracy = accuracy_score(y_test, y_pred)
accuracy
precision recall f1-score support
0 0.99 0.99 0.99 322
3 0.92 0.92 0.92 38
accuracy 0.98 360
macro avg 0.96 0.96 0.96 360
weighted avg 0.98 0.98 0.98 360
Out[43]:
0.9833333333333333
In [44]:
# 길이는 y_pred와 같으면서 0으로만 이루어진 리스트를 fake_pred 라는 변수로 저장해 보고, 이 리스트와 실제 정답인 y_test간의 정확도를 확인
fake_pred = [0] * len(y_pred)
accuracy = accuracy_score(y_test, fake_pred)
accuracy
Out[44]:
0.8944444444444445
그래서 정확도 뿐만아니라 다른 척도들도 사용함¶
In [53]:
from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, y_pred)
#각각은 왼쪽 위부터 순서대로 TP,FN,FP,TN의 개수를 나타냅니다. 특히, 손글씨 문제에서의 `0`은 Positive 역할을, `3`은 Negative 역할을 합니다.
Out[53]:
array([[319, 3],
[ 3, 35]])
In [54]:
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
precision recall f1-score support
0 0.99 0.99 0.99 322
3 0.92 0.92 0.92 38
accuracy 0.98 360
macro avg 0.96 0.96 0.96 360
weighted avg 0.98 0.98 0.98 360
In [55]:
confusion_matrix(y_test, fake_pred)
Out[55]:
array([[322, 0],
[ 38, 0]])
In [57]:
print(classification_report(y_test, fake_pred))
# 3 에 대한 정확도가 0임을 알수 있다
precision recall f1-score support
0 0.89 1.00 0.94 322
3 0.00 0.00 0.00 38
accuracy 0.89 360
macro avg 0.45 0.50 0.47 360
weighted avg 0.80 0.89 0.84 360
In [59]:
accuracy_score(y_test, y_pred), accuracy_score(y_test, fake_pred)
# 정확도에는 별 문제가 없다
Out[59]:
(0.9833333333333333, 0.8944444444444445)
3-1 손글씨를 분류¶
In [96]:
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import LogisticRegression
digits = load_digits()
digits_data = digits.data
digits_label = digits.target
# 데이터 지정
X_train, X_test, y_train, y_test = train_test_split(digits_data,
digits_label,
test_size=0.2,
random_state=40)
# targetname 출력
print(digits.target_names)
# 의사결정트리
decision_tree = DecisionTreeClassifier(random_state=40)
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_test)
print(classification_report(y_test, y_pred))
# 랜덤포레스트
random_forest = RandomForestClassifier(random_state=40)
random_forest.fit(X_train, y_train)
y_pred = random_forest.predict(X_test)
print(classification_report(y_test, y_pred))
# S V M
svm_model = svm.SVC()
print(svm_model._estimator_type)
svm_model.fit(X_train, y_train)
y_pred = svm_model.predict(X_test)
print(classification_report(y_test, y_pred))
# SGD Classifier 확률적 경사하강법
sgd_model = SGDClassifier()
print(sgd_model._estimator_type)
sgd_model.fit(X_train, y_train)
y_pred = sgd_model.predict(X_test)
print(classification_report(y_test, y_pred))
# Logistic Regression 로지스틱 회귀
logistic_model = LogisticRegression(max_iter=10000)
print(logistic_model._estimator_type)
logistic_model.fit(X_train, y_train)
y_pred = logistic_model.predict(X_test)
print(classification_report(y_test, y_pred))
[0 1 2 3 4 5 6 7 8 9]
precision recall f1-score support
0 0.97 1.00 0.98 30
1 0.77 0.72 0.75 47
2 0.81 0.79 0.80 38
3 0.74 0.68 0.71 34
4 0.90 0.92 0.91 38
5 0.83 0.86 0.84 28
6 0.97 0.97 0.97 34
7 0.81 0.86 0.83 35
8 0.76 0.72 0.74 36
9 0.73 0.80 0.76 40
accuracy 0.82 360
macro avg 0.83 0.83 0.83 360
weighted avg 0.82 0.82 0.82 360
precision recall f1-score support
0 0.97 1.00 0.98 30
1 0.96 1.00 0.98 47
2 1.00 0.97 0.99 38
3 0.97 1.00 0.99 34
4 0.97 0.97 0.97 38
5 0.96 0.96 0.96 28
6 1.00 0.97 0.99 34
7 0.95 1.00 0.97 35
8 0.97 0.94 0.96 36
9 1.00 0.93 0.96 40
accuracy 0.97 360
macro avg 0.98 0.98 0.97 360
weighted avg 0.98 0.97 0.97 360
classifier
precision recall f1-score support
0 1.00 1.00 1.00 30
1 0.96 1.00 0.98 47
2 1.00 1.00 1.00 38
3 1.00 1.00 1.00 34
4 1.00 1.00 1.00 38
5 1.00 1.00 1.00 28
6 1.00 1.00 1.00 34
7 1.00 1.00 1.00 35
8 0.97 0.94 0.96 36
9 1.00 0.97 0.99 40
accuracy 0.99 360
macro avg 0.99 0.99 0.99 360
weighted avg 0.99 0.99 0.99 360
classifier
precision recall f1-score support
0 1.00 1.00 1.00 30
1 0.87 0.96 0.91 47
2 1.00 1.00 1.00 38
3 1.00 0.94 0.97 34
4 1.00 1.00 1.00 38
5 1.00 1.00 1.00 28
6 0.97 1.00 0.99 34
7 0.97 0.94 0.96 35
8 0.97 0.86 0.91 36
9 0.93 0.95 0.94 40
accuracy 0.96 360
macro avg 0.97 0.97 0.97 360
weighted avg 0.97 0.96 0.96 360
classifier
precision recall f1-score support
0 1.00 1.00 1.00 30
1 0.98 0.91 0.95 47
2 0.97 1.00 0.99 38
3 0.97 0.94 0.96 34
4 1.00 0.97 0.99 38
5 0.96 0.96 0.96 28
6 0.97 1.00 0.99 34
7 0.94 0.97 0.96 35
8 0.94 0.92 0.93 36
9 0.91 0.97 0.94 40
accuracy 0.96 360
macro avg 0.97 0.97 0.97 360
weighted avg 0.96 0.96 0.96 360
서포트 벡터 머신이 가장 정확하게 나옴¶
이유 : 숫자는 참인 것을 참이라고 판단할 때 의미가 있다고 생각하여 평가 지표로 Recall을 선택.
3-2 와인 분류¶
In [105]:
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import LogisticRegression
wine = load_wine()
wine_data = wine.data
wine_label = wine.target
X_train, X_test, y_train, y_test = train_test_split(wine_data,
wine_label,
test_size=0.2,
random_state=30)
# targetname 출력
print(wine.target_names)
# 의사결정트리
decision_tree = DecisionTreeClassifier(random_state=30)
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_test)
print(classification_report(y_test, y_pred))
# 랜덤포레스트
random_forest = RandomForestClassifier(random_state=30)
random_forest.fit(X_train, y_train)
y_pred = random_forest.predict(X_test)
print(classification_report(y_test, y_pred))
# S V M
svm_model = svm.SVC()
print(svm_model._estimator_type)
svm_model.fit(X_train, y_train)
y_pred = svm_model.predict(X_test)
print(classification_report(y_test, y_pred))
# SGD Classifier 확률적 경사하강법
sgd_model = SGDClassifier()
print(sgd_model._estimator_type)
sgd_model.fit(X_train, y_train)
y_pred = sgd_model.predict(X_test)
print(classification_report(y_test, y_pred))
# Logistic Regression 로지스틱 회귀
logistic_model = LogisticRegression(max_iter=5000)
print(logistic_model._estimator_type)
logistic_model.fit(X_train, y_train)
y_pred = logistic_model.predict(X_test)
print(classification_report(y_test, y_pred))
['class_0' 'class_1' 'class_2']
precision recall f1-score support
0 0.83 0.91 0.87 11
1 0.81 0.81 0.81 16
2 0.88 0.78 0.82 9
accuracy 0.83 36
macro avg 0.84 0.83 0.84 36
weighted avg 0.83 0.83 0.83 36
precision recall f1-score support
0 0.92 1.00 0.96 11
1 1.00 0.94 0.97 16
2 1.00 1.00 1.00 9
accuracy 0.97 36
macro avg 0.97 0.98 0.97 36
weighted avg 0.97 0.97 0.97 36
classifier
precision recall f1-score support
0 0.89 0.73 0.80 11
1 0.79 0.69 0.73 16
2 0.46 0.67 0.55 9
accuracy 0.69 36
macro avg 0.71 0.69 0.69 36
weighted avg 0.74 0.69 0.71 36
classifier
precision recall f1-score support
0 1.00 0.55 0.71 11
1 1.00 0.12 0.22 16
2 0.32 1.00 0.49 9
accuracy 0.47 36
macro avg 0.77 0.56 0.47 36
weighted avg 0.83 0.47 0.44 36
classifier
precision recall f1-score support
0 0.92 1.00 0.96 11
1 1.00 0.94 0.97 16
2 1.00 1.00 1.00 9
accuracy 0.97 36
macro avg 0.97 0.98 0.97 36
weighted avg 0.97 0.97 0.97 36
랜덤포레스트 분류가 적당함¶
이유 : 어떠한 와인인지 알아내는것이 중요하기때문에 랜덤포레스트와 로지스틱회귀가 같은 값이 나왔으나 분류하되 3번 분류를 진행해야하기에 판단함
3-3 유방암 진단 분류¶
In [103]:
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import LogisticRegression
breast_cancer = load_breast_cancer()
breast_cancer_data = breast_cancer.data
breast_cancer_label = breast_cancer.target
X_train, X_test, y_train, y_test = train_test_split(breast_cancer_data,
breast_cancer_label,
test_size=0.2,
random_state=40)
# targetname 출력
print(breast_cancer.target_names)
# 의사결정트리
decision_tree = DecisionTreeClassifier(random_state=40)
decision_tree.fit(X_train, y_train)
y_pred = decision_tree.predict(X_test)
print(classification_report(y_test, y_pred))
# 랜덤포레스트
random_forest = RandomForestClassifier(random_state=40)
random_forest.fit(X_train, y_train)
y_pred = random_forest.predict(X_test)
print(classification_report(y_test, y_pred))
# S V M
svm_model = svm.SVC()
print(svm_model._estimator_type)
svm_model.fit(X_train, y_train)
y_pred = svm_model.predict(X_test)
print(classification_report(y_test, y_pred))
# SGD Classifier 확률적 경사하강법
sgd_model = SGDClassifier()
print(sgd_model._estimator_type)
sgd_model.fit(X_train, y_train)
y_pred = sgd_model.predict(X_test)
print(classification_report(y_test, y_pred))
# Logistic Regression 로지스틱 회귀
logistic_model = LogisticRegression(max_iter=5000)
print(logistic_model._estimator_type)
logistic_model.fit(X_train, y_train)
y_pred = logistic_model.predict(X_test)
print(classification_report(y_test, y_pred))
['malignant' 'benign']
precision recall f1-score support
0 0.93 0.97 0.95 39
1 0.99 0.96 0.97 75
accuracy 0.96 114
macro avg 0.96 0.97 0.96 114
weighted avg 0.97 0.96 0.97 114
precision recall f1-score support
0 0.90 0.97 0.94 39
1 0.99 0.95 0.97 75
accuracy 0.96 114
macro avg 0.95 0.96 0.95 114
weighted avg 0.96 0.96 0.96 114
classifier
precision recall f1-score support
0 0.92 0.87 0.89 39
1 0.94 0.96 0.95 75
accuracy 0.93 114
macro avg 0.93 0.92 0.92 114
weighted avg 0.93 0.93 0.93 114
classifier
precision recall f1-score support
0 1.00 0.77 0.87 39
1 0.89 1.00 0.94 75
accuracy 0.92 114
macro avg 0.95 0.88 0.91 114
weighted avg 0.93 0.92 0.92 114
classifier
precision recall f1-score support
0 0.95 0.97 0.96 39
1 0.99 0.97 0.98 75
accuracy 0.97 114
macro avg 0.97 0.97 0.97 114
weighted avg 0.97 0.97 0.97 114
로지스틱 회귀가 적합함¶
유방암의 경우 양성과 음성을 정확하게 판단하는것이 중요하다고 생각하여 accuracy 로 판단함
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