提高机器学习模型的准确性

Question

所以我用粒子群优化技术和支持向量机创建了这个预测糖尿病的模型，并得到了 82.4% 的准确率。但我可以在其他研究中看到使用与我相同的数据集（pima 印度糖尿病数据集）的准确性超过 90%，请问我如何改进我的模型以获得更高的准确性

Answer 1

this is my code

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.svm import SVC
import pandas as pd
import pickle
from niapy.problems import Problem
from niapy.task import Task
from niapy.algorithms.basic import ParticleSwarmOptimization
from sklearn.metrics import recall_score, roc_auc_score, f1_score, precision_score

class SVMFeatureSelection(Problem):
    def __init__(self, X_train, y_train, alpha=0.99):
        super().__init__(dimension=X_train.shape[1], lower=0, upper=1)
        self.X_train = X_train
        self.y_train = y_train
        self.alpha = alpha

    def _evaluate(self, x):
        selected = x > 0.5
        num_selected = selected.sum()
        if num_selected == 0:
            return 1.0
        accuracy = cross_val_score(SVC(), self.X_train[:, selected], 
        self.y_train, cv=2, n_jobs=-1).mean()
        score = 1 - accuracy
        num_features = self.X_train.shape[1]
        return self.alpha * score + (1 - self.alpha) * (num_selected / 
num_features)

# load the dataset, run the algorithm and compare the results
data = pd.read_csv("diabetes.csv")
X = data.iloc[:, :8]
X = X.values

y = data["Outcome"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
stratify=y, random_state=1234)

problem = SVMFeatureSelection(X_train, y_train)
task = Task(problem, max_iters=100)
algorithm = ParticleSwarmOptimization(population_size=10, seed=500)
best_features, best_fitness = algorithm.run(task)

selected_features = best_features > 0.5
print('Number of selected features:', selected_features.sum())

model_selected = SVC()
# model_all = SVC()

model_selected.fit(X_train[:, selected_features], y_train)
print('The accuracy score:', model_selected.score(X_test[:, selected_features], 
y_test))

y_pred = model_selected.predict(X_train[:, selected_features])

# model_selected.fit(X_train, y_train)

roc = roc_auc_score(y_train, y_pred)

print("The recall score = ", recall_score(y_train, y_pred, average='macro'))
print("The Precision = ",precision_score(y_train, y_pred, average='macro'))
print("The f1_score = ",f1_score(y_train, y_pred, average='macro'))
print("The ROC value = ",roc)

filename = 'diabetes_model.sav'
pickle.dump(model_selected, open(filename, 'wb'))