RandomTreesInteract.ipynb

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   "execution_count": 31,
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   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
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      "text/plain": [
       "interactive(children=(IntSlider(value=50, continuous_update=False, description='_n_samples', max=300, min=20),…"
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   "source": [
    "def plot_boundary(clf, X, y):\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                         np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "    f, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    ax.contourf(xx, yy, Z, alpha=0.9)\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor='k')\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "def control(_n_samples, _noise, _n_trees, _max_depth, _criterion):\n",
    "    x,y = make_moons(_n_samples, noise = _noise, random_state=0)\n",
    "    clf = RandomForestClassifier(random_state=23, n_estimators = _n_trees, \n",
    "                                 max_depth = _max_depth, criterion = _criterion).fit(x,y)\n",
    "    plot_boundary(clf, x, y)\n",
    "    \n",
    "interact(control, _n_samples = widgets.IntSlider(min=20,max=300,step=1,value=50, continuous_update = False),\n",
    "                  _noise = widgets.FloatSlider(min=0,max=3,step=0.1,value=0.1, continuous_update = False), \n",
    "                  _n_trees = widgets.IntSlider(min=2,max=120,step=1,value=2, continuous_update = False),\n",
    "                  _max_depth = widgets.IntSlider(min=2,max=10,step=1,value=4, continuous_update = False),\n",
    "                  _criterion = ['gini','entropy']\n",
    "        );"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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