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Scikit-discendum (sklearn) popularis apparatus est bibliothecae discendae, quae multa instrumenta ad fodienda et analysin data praebet. Simplex forma fundamentalis in sklearn est, quae introducit quomodo notitias faciendas praeprocessionis, exemplar disciplinae et aestimationis.

1. institutionem et import

Primum fac tibi bibliothecam sklearn inauguratam habere. Potest installed per pituitam:

pip install scikit-learn

Sklearn importans plerumque utitur hoc modo:

import sklearn from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score

2. Onerantes notitia paro

sklearn continet aliquas notas constructas-in normali ad faciliorem usum et eruditionem nostram. Exempli gratia iris dataset onerare possumus:

iris = datasets.load_iris() X = iris.data # 特征数据 y = iris.target # 目标数据

3. Data preprocessing

Priusquam exemplar instituatur, notitia plerumque praeprocedere debet, ut normaizationis, normalizationis, pluma lectionis, etc.

normalized data：

scaler = StandardScaler() X_scaled = scaler.fit_transform(X)

4. divide disciplina set et test paro

Dividere notitias positas in disciplina paro et test paro, plerumque utendo train_test_split officium:

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)

5. Select exemplum et disciplina

Aptum exemplar ad formandum elige, ut machina vectoris sustentans (SVM);

from sklearn.svm import SVC model = SVC(kernel='linear', C=1.0) model.fit(X_train, y_train)

6. Model iudicium

Testimento utendo ad exemplar faciendum perpendere, indicibus uti potes ac accurate:

y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f'Accuracy: {accuracy}')

7. Parameter tuning ac crucis-validationis

Utere crucis-saltatione ad optimize exemplar parametri:

from sklearn.model_selection import GridSearchCV parameters = {'kernel': ('linear', 'rbf'), 'C': [1, 10]} svc = SVC() clf = GridSearchCV(svc, parameters) clf.fit(X_train, y_train) print(clf.best_params_)

Hoc simplex doceo ostendit quomodo sklearn utatur ad operas discendi apparatus fundamentales. sklearn opes instrumentorum et algorithmorum praebet quae variis machinae quaestionibus discendi solvendis applicari possunt. Applicatio specifica pendet a notitia tua et in certis functionibus requisitis.