不好意思，不知道该如何最好地表达标题…
我刚开始学习Python机器学习，仍在学习中。我有一组数据集（ML_TEST）：
Sale ID Amount in $ Region Product Salesperson Win_Lose 1 500 North ink Jon 1 2 250 North ink Jon 0 3 250 North ink Jon 0 4 750 North paper Jon 0 5 800 North ink Bill 0 6 250 North paper Bill 1 7 750 North paper Jon 1 8 250 North ink Bill 1 9 250 North paper Dave 0 10 800 North desk chair Bill 1 11 750 South paper Dave 0 12 500 South desk chair Dave 1 13 500 South ink Bill 1 14 500 South ink Bill 0 15 400 South paper Jon 0 16 250 South paper Jon 0 17 250 South ink Jon 1 18 250 East ink Dave 1 19 250 East ink Bill 1 20 400 East ink Jon 0 21 400 East paper Dave 1 22 500 West desk chair Bill 0 23 750 West desk chair Jon 1 24 800 West desk chair Jon 0 25 450 West paper Jon 0 26 250 West ink Dave 1 27 250 West paper Dave 1 28 250 West paper Bill 1 29 250 West paper Bill 0 30 400 West ink Bill 1我正在尝试理解运行以下代码时出现的错误：
#Load Librariesimport pandasfrom pandas.tools.plotting import scatter_matriximport matplotlib.pyplot as pltfrom sklearn import model_selectionfrom sklearn.metrics import classification_reportfrom sklearn.metrics import confusion_matrixfrom sklearn.metrics import accuracy_scorefrom sklearn.linear_model import LogisticRegressionfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysisfrom sklearn.naive_bayes import GaussianNBfrom sklearn.svm import SVCimport pyodbcconn = pyodbc.connect('')sql = "Select * from TMP.ML_TEST"dataset = pd.read_sql(sql, conn)array = dataset.valuesX = array[:,0:5]Y = array[:,5]validation_size = 0.20seed = 7X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)print(Y)seed = 7scoring = 'accuracy'models = []models.append(('LR', LogisticRegression()))models.append(('LDA', LinearDiscriminantAnalysis()))models.append(('KNN', KNeighborsClassifier()))models.append(('CART', DecisionTreeClassifier()))models.append(('NB', GaussianNB()))models.append(('SVM', SVC()))# evaluate each model in turnresults = []names = []for name, model in models: kfold = model_selection.KFold(n_splits=12, random_state=seed) cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) results.append(cv_results) names.append(name) msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) print(msg)以下是我得到的错误信息：
ValueError Traceback (most recent call last)<ipython-input-119-86bed78dded1> in <module>() 12 for name, model in models: 13 kfold = model_selection.KFold(n_splits=12, random_state=seed)---> 14 cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) 15 results.append(cv_results) 16 names.append(name)C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch) 138 train, test, verbose, None, 139 fit_params)--> 140 for train, test in cv_iter) 141 return np.array(scores)[:, 0] 142 C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable) 756 # was dispatched. In particular this covers the edge 757 # case of Parallel used with an exhausted iterator.--> 758 while self.dispatch_one_batch(iterator): 759 self._iterating = True 760 else:C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator) 606 return False 607 else:--> 608 self._dispatch(tasks) 609 return True 610 C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch) 569 dispatch_timestamp = time.time() 570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)--> 571 job = self._backend.apply_async(batch, callback=cb) 572 self._jobs.append(job) 573 C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback) 107 def apply_async(self, func, callback=None): 108 """Schedule a func to be run"""--> 109 result = ImmediateResult(func) 110 if callback: 111 callback(result)C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch) 324 # Don't delay the application, to avoid keeping the input 325 # arguments in memory--> 326 self.results = batch() 327 328 def get(self):C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self) 129 130 def __call__(self):--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] 132 133 def __len__(self):C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0) 129 130 def __call__(self):--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] 132 133 def __len__(self):C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score) 236 estimator.fit(X_train, **fit_params) 237 else:--> 238 estimator.fit(X_train, y_train, **fit_params) 239 240 except Exception as e:C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight) 1171 1172 X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,-> 1173 order="C") 1174 check_classification_targets(y) 1175 self.classes_ = np.unique(y)C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator) 519 X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite, 520 ensure_2d, allow_nd, ensure_min_samples,--> 521 ensure_min_features, warn_on_dtype, estimator) 522 if multi_output: 523 y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator) 380 force_all_finite) 381 else:--> 382 array = np.array(array, dtype=dtype, order=order, copy=copy) 383 384 if ensure_2d:ValueError: could not convert string to float: 'Jon'我真的很想使用朴素贝叶斯模型，因为我的很多特征都是文本，但连这个错误都无法解决 🙁
我正在尝试构建一个模型，根据这些特征预测一笔销售是赢还是输。
回答：
