Split data sklearn.ipynb

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   "source": [
    "from sklearn import datasets\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import cross_validation\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "labels = iris.target"
   ]
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    "### import the relevant code and make your train/test split\n",
    "### name the output datasets features_train, features_test,\n",
    "### labels_train, and labels_test\n",
    "\n",
    "### set the random_state to 0 and the test_size to 0.4 so\n",
    "### we can exactly check your result\n",
    "features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)\n",
    "\n"
   ]
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      "0.966666666667\n"
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   "source": [
    "clf = SVC(kernel=\"linear\", C=1.)\n",
    "clf.fit(features_train, labels_train)\n",
    "\n",
    "print clf.score(features_test, labels_test)\n"
   ]
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       "((150L, 4L), (150L,))"
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   "source": [
    "# the new version...\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn import svm\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "iris.data.shape, iris.target.shape"
   ]
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    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris.data, iris.target, test_size=0.4, random_state=0)\n",
    "\n",
    "X_train.shape, y_train.shape\n",
    "\n",
    "X_test.shape, y_test.shape\n",
    "\n",
    "\n",
    "clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)\n",
    "clf.score(X_test, y_test) "
   ]
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