不均衡データを3つのクラスに分類し、グリッドサーチをしたいのですが上手くいきません。

前提・実現したいこと

不均衡データを3つのクラスに分類し、グリッドサーチをしたいのですが
上手くいきません。

発生している問題・エラーメッセージ

ValueError                                Traceback (most recent call last)
<ipython-input-15-32a008acd626> in <module>
     41 grid_search = GridSearchCV(LinearSVC(class_weight="balanced"), param_grid, cv = 4)
     42 
---> 43 grid_search.fit(X_train_std, y_train)
     44 
     45 #　結果

~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    686                 return results
    687 
--> 688             self._run_search(evaluate_candidates)
    689 
    690         # For multi-metric evaluation, store the best_index_, best_params_ and

~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
   1147     def _run_search(self, evaluate_candidates):
   1148         """Search all candidates in param_grid"""
-> 1149         evaluate_candidates(ParameterGrid(self.param_grid))
   1150 
   1151 

~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)
    665                                for parameters, (train, test)
    666                                in product(candidate_params,
--> 667                                           cv.split(X, y, groups)))
    668 
    669                 if len(out) < 1:

~\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
    919             # remaining jobs.
    920             self._iterating = False
--> 921             if self.dispatch_one_batch(iterator):
    922                 self._iterating = self._original_iterator is not None
    923 

~\Anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    757                 return False
    758             else:
--> 759                 self._dispatch(tasks)
    760                 return True
    761 

~\Anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    714         with self._lock:
    715             job_idx = len(self._jobs)
--> 716             job = self._backend.apply_async(batch, callback=cb)
    717             # A job can complete so quickly than its callback is
    718             # called before we get here, causing self._jobs to

~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    180     def apply_async(self, func, callback=None):
    181         """Schedule a func to be run"""
--> 182         result = ImmediateResult(func)
    183         if callback:
    184             callback(result)

~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    547         # Don't delay the application, to avoid keeping the input
    548         # arguments in memory
--> 549         self.results = batch()
    550 
    551     def get(self):

~\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~\Anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~\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, return_estimator, error_score)
    501     train_scores = {}
    502     if parameters is not None:
--> 503         estimator.set_params(**parameters)
    504 
    505     start_time = time.time()

~\Anaconda3\lib\site-packages\sklearn\base.py in set_params(self, **params)
    222                                  'Check the list of available parameters '
    223                                  'with `estimator.get_params().keys()`.' %
--> 224                                  (key, self))
    225 
    226             if delim:

ValueError: Invalid parameter gamma for estimator LinearSVC(C=0.001, class_weight='balanced', dual=True, fit_intercept=True,
          intercept_scaling=1, loss='squared_hinge', max_iter=1000,
          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
          verbose=0). Check the list of available parameters with `estimator.get_params().keys()`.


```Python
ソースコード

#　データ加工・処理・分析ライブラリ
import numpy as np
import numpy.random as random
import scipy as sp
from pandas import Series, DataFrame
import pandas as pd

#　可視化ライブラリ
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns

%matplotlib inline
%precision 3

#　機械学習ライブラリ
import sklearn
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import StandardScaler

#　データの読み込み
data = pd.read_csv("2017現場加速度計測_教師データまとめ.csv", encoding="shift_jis")
data.assign(生産性分類=pd.to_numeric(data.生産性分類))

#　訓練データとテストデータに分ける
X = data[["km", "平均合成加速度", "sx", "sy", "sz", "mean_x", "mean_y", "mean_z"]]
y = data["生産性分類"]
X_train, X_test, y_train, y_test = train_test_split(X, y,stratify = y, random_state=0)

#　標準化
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

#　学習(SVMによる分類とグリッドサーチ)
param_grid = {"C": [0.001, 0.01, 0.1, 1, 10, 100], "gamma": [0.001, 0.01, 0.1, 1, 10, 100]}
grid_search = GridSearchCV(LinearSVC(class_weight="balanced"), param_grid, cv = 4)

grid_search.fit(X_train_std, y_train)

#　結果
print("Test set score: {:.3f}".format(grid_search.score(X_test_std, y_test)))
print("Best parameters: {:.3f}".format(grid_search.best_params_))
print("Best cross-validation score: {:.3f}".format(grid_search.best_score_))