Update _Dist/NeuralNetworks/b_TraditionalML

Author: carefree0910

_Dist/NeuralNetworks/b_TraditionalML/TraditionalML.ipynb

@@ -23,8 +23,8 @@
     "\n",
     "from sklearn.preprocessing import OneHotEncoder\n",
     "enc = OneHotEncoder()\n",
-    "nb_x_train = enc.fit_transform(x_train)\n",
-    "nb_x_test = enc.transform(x_test)"
+    "x_train_one_hot = enc.fit_transform(x_train)\n",
+    "x_test_one_hot = enc.transform(x_test)"
    ]
   },
   {
@@ -46,8 +46,8 @@
     "from sklearn.naive_bayes import MultinomialNB\n",
     "\n",
     "clf = MultinomialNB()\n",
-    "clf.fit(nb_x_train, y_train)\n",
-    "print(np.mean(y_test == clf.predict(nb_x_test)))"
+    "clf.fit(x_train_one_hot, y_train)\n",
+    "print(np.mean(y_test == clf.predict(x_test_one_hot)))"
    ]
   },
   {
@@ -61,6 +61,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "1.0\n",
       "1.0\n"
      ]
     }
@@ -69,8 +70,12 @@
     "from sklearn.tree import DecisionTreeClassifier\n",
     "\n",
     "clf = DecisionTreeClassifier()\n",
+    "\n",
     "clf.fit(x_train, y_train)\n",
-    "print(np.mean(y_test == clf.predict(x_test)))"
+    "print(np.mean(y_test == clf.predict(x_test)))\n",
+    "\n",
+    "clf.fit(x_train_one_hot, y_train)\n",
+    "print(np.mean(y_test == clf.predict(x_test_one_hot)))"
    ]
   },
   {
@@ -84,16 +89,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.0\n"
+      "1.0\n",
+      "0.998116760829\n"
      ]
     }
    ],
    "source": [
     "from sklearn.svm import SVC\n",
     "\n",
     "clf = SVC()\n",
+    "\n",
     "clf.fit(x_train, y_train)\n",
-    "print(np.mean(y_test == clf.predict(x_test)))"
+    "print(np.mean(y_test == clf.predict(x_test)))\n",
+    "\n",
+    "clf.fit(x_train_one_hot, y_train)\n",
+    "print(np.mean(y_test == clf.predict(x_test_one_hot)))"
    ]
   },
   {
@@ -107,16 +117,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.956214689266\n"
+      "0.961393596987\n",
+      "0.999529190207\n"
      ]
     }
    ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "\n",
     "clf = LogisticRegression()\n",
+    "\n",
     "clf.fit(x_train, y_train)\n",
-    "print(np.mean(y_test == clf.predict(x_test)))"
+    "print(np.mean(y_test == clf.predict(x_test)))\n",
+    "\n",
+    "clf.fit(x_train_one_hot, y_train)\n",
+    "print(np.mean(y_test == clf.predict(x_test_one_hot)))"
    ]
   }
  ],
@@ -141,5 +156,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 0
 }