✔️ #⃣ 🌥️ きのこ（機械学習） 🗺️ 🐰 🍽️

みなさん、こんにちは！きのこのデータを検討し、その食用性を予測し、相関関係を構築するなど、さまざまなことを行います。

https://www.kaggle.com/uciml/mushroom-classificationのKaggleのキノコに関するデータ（元のデータフレーム）を使用します。2つの追加のデータフレームが記事に添付されます。

すべての操作はhttps://colab.research.google.com/notebooks/intro.ipynbで行われます

#  e    
import pandas as pd

#     ,     confusion_matrix:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix

#    :
import matplotlib.pyplot as plt
import seaborn as sns

#   
mushrooms = pd.read_csv('/content/mushrooms.csv')

#  
mushrooms.head()
#           :

#  
mushrooms.info()

#     
mushrooms.shape

#    LabelEncoder          (  heatmap)
#     ,           
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
for i in mushrooms.columns:
    mushrooms[i]=le.fit_transform(mushrooms[i])

#     
mushrooms.head()

#       heatmap
fig = plt.figure(figsize=(18, 14))
sns.heatmap(mushrooms.corr(), annot = True, vmin=-1, vmax=1, center= 0, cmap= 'coolwarm', linewidths=3, linecolor='black')
fig.tight_layout()
plt.show()

: (veil-color,gill-spacing) = +0.9 (ring-type,bruises) = +0.69 (ring-type,gill-color) = +0.63 (spore-print-color,gill-size) = +0.62 (stalk-root,spore-print-color) = -0.54 (population,gill-spacing) = -0.53 (gill-color,class) = -0.53 , . , , .

#  ,   .
X = mushrooms.drop(['class'], axis=1)
#  ,   .
y = mushrooms['class']

#   RandomForestClassifier.
rf = RandomForestClassifier(random_state=0)

#   ,            
#{'n_estimators': range(10, 51, 10), 'max_depth': range(1, 13, 2),
#             'min_samples_leaf': range(1,8), 'min_samples_split': range(2,10,2)}
parameters = {'n_estimators': [10], 'max_depth': [7],
              'min_samples_leaf': [1], 'min_samples_split': [2]}

#  Random forest  GridSearchCV.
GridSearchCV_clf = GridSearchCV(rf, parameters, cv=3, n_jobs=-1)
GridSearchCV_clf.fit(X, y)

#   ,          
best_clf = GridSearchCV_clf.best_params_

#   .
best_clf

#  confusion matrix ( )  ,          .
y_true = pd.read_csv ('/content/testing_y_mush.csv')
sns.heatmap(confusion_matrix(y_true, predictions), annot=True, cmap="Blues")
plt.show()

このエラーのマトリックスは、最初のタイプのエラーがないことを示していますが、値3には2番目のタイプのエラーがあります。これは、このモデルでは0になりがちな非常に低い指標です。

次に、dfの最高の精度でモデルを決定するための操作を実行します

#     
from sklearn.metrics import accuracy_score
mr = accuracy_score(y_true, predictions)


#     
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 = 0)

# 
# 
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(max_iter = 10000)
lr.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = lr.predict(x_test)
cm = confusion_matrix(y_test,y_pred)


#  
log_reg = accuracy_score(y_test,y_pred)


#K  
# 
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski',p = 2)
knn.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = knn.predict(x_test)
cm = confusion_matrix(y_test,y_pred)


#  
from sklearn.metrics import accuracy_score
knn_1 = accuracy_score(y_test,y_pred)


# 
# 
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(criterion = 'entropy')
dt.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = dt.predict(x_test)
cm = confusion_matrix(y_test,y_pred)

#  
from sklearn.metrics import accuracy_score
dt_1 = accuracy_score(y_test,y_pred)


#  
# 
from sklearn.naive_bayes import GaussianNB
nb = GaussianNB()
nb.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = nb.predict(x_test)
cm = confusion_matrix(y_test,y_pred)


#  
from sklearn.metrics import accuracy_score
nb_1 = accuracy_score(y_test,y_pred)


#  
plt.figure(figsize= (16,12))
ac = [log_reg,knn_1,nb_1,dt_1,mr]
name = [' ','  ','  ',' ', ' ']
sns.barplot(x = ac,y = name,palette='colorblind')
plt.title("  ", fontsize=20, fontweight="bold")

予測の最も正確なモデルは決定木であると結論付けることができます。

きのこ（機械学習）

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