编辑：

所以我设法用所有的建议来修正错误。但是现在模型.预测部分是给我这个问题。在Expected 2D array, got 1D array instead:

array=[ 12 15432 40 20 33 40000 12800 20 19841 0 0].

Reshape your data either using array.reshape(-1, 1) if your data has a

single feature or array.reshape(1, -1) if it contains a single sample.

这是我正在使用的新代码

^{pr2}$

旧：

我在这里要做的是使用一个动物特征的列表，比如牙齿和大小，然后使用一些内置的模型，比如SVN kn ect和我制作的cvs数据集。但是它一直说它不能把字符串转换成float，当我去掉cvs中的所有字符串时，它确实起作用了，但是我不知道这是否是我想要的，因为我想把每一只动物描绘成猎人或食腐动物。我真的不知道我做错了什么，因为我是python新手。也许有人能帮上忙，看看我的代码，告诉我我哪里做错了。此外，任何改善这一点的建议都会被欣然接受。在

所以我的代码是这样的：

^{3}$

现在这做了很多，我认为我把所有的事情都做对了，但可能我的数据也是错误的。在

这是测试csv文件Name,teethLength,weight,length,hieght,speed,Calorie Intake,Bite Force,Prey Speed,PreySize,EyeSight,Smell,Class

Crocodile,4,2400,23,1.6,8,2500,3700,30,881,0,0,Primary Hunter

Lion,2.7,416,9.8,3.9,50,7236,650,35,1300,0,0,Primary Hunter

Bear,3.6,600,7,3.35,40,20000,975,0,0,0,0,Primary Scavenger

Tiger,3,260,12,3,40,7236,1050,37,160,0,0,Primary Hunter

Hyena,0.27,160,5,2,37,5000,1100,20,40,0,0,Primary Scavenger

Jaguar,2,220,5.5,2.5,40,5000,1350,15,300,0,0,Primary Hunter

Cheetah,1.5,154,4.9,2.9,70,2200,475,56,185,0,0,Primary Hunter

KomodoDragon,0.4,150,8.5,1,13,1994,240,24,110,0,0,Primary Scavenger

如有任何帮助，我们将不胜感激。在

堆栈跟踪File "", line 1, in

runfile('E:/TestPythonCode/Classifier.py', wdir='E:/TestPythonCode')

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 678, in runfile

execfile(filename, namespace)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 106, in execfile

exec(compile(f.read(), filename, 'exec'), namespace)

File "E:/TestPythonCode/Classifier.py", line 110, in

cv_results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\model_selection\_validation.py", line 342, in cross_val_score

pre_dispatch=pre_dispatch)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\model_selection\_validation.py", line 206, in cross_validate

for train, test in cv.split(X, y, groups))

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 779, in __call__

while self.dispatch_one_batch(iterator):

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 625, in dispatch_one_batch

self._dispatch(tasks)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 588, in _dispatch

job = self._backend.apply_async(batch, callback=cb)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 111, in apply_async

result = ImmediateResult(func)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 332, in __init__

self.results = batch()

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__

return [func(*args, **kwargs) for func, args, kwargs in self.items]

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in

return [func(*args, **kwargs) for func, args, kwargs in self.items]

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\model_selection\_validation.py", line 458, in _fit_and_score

estimator.fit(X_train, y_train, **fit_params)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\linear_model\logistic.py", line 1216, in fit

order="C")

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site-packages\sklearn\utils\validation.py", line 573, in check_X_y

ensure_min_features, warn_on_dtype, estimator)

File "C:\Users\matth\Anaconda3\envs\TensorfGPU2\lib\site- packages\sklearn\utils\validation.py", line 433, in check_array

array = np.array(array, dtype=dtype, order=order, copy=copy)

ValueError: could not convert string to float: 'KomodoDragon'