added html files

Author: mhjensen

doc/pub/DecisionTrees/html/._DecisionTrees-bs054.html

@@ -2431,9 +2431,8 @@ <h2 id="___sec55">Regression Case </h2>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scikitplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skplt</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> mean_squared_error
 
-n = <span style="color: #B452CD">40</span>
-n_boostraps = <span style="color: #B452CD">100</span>
-maxdegree = <span style="color: #B452CD">8</span>
+n = <span style="color: #B452CD">100</span>
+maxdegree = <span style="color: #B452CD">6</span>
 
 <span style="color: #228B22"># Make data set.</span>
 x = np.linspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">3</span>, n).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
@@ -2450,8 +2449,8 @@ <h2 id="___sec55">Regression Case </h2>
 X_test_scaled = scaler.transform(X_test)
 
 <span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(maxdegree):
-    model =  xgb.XGBRegressor(objective =<span style="color: #CD5555">&#39;reg:linear&#39;</span>, colsample_bytree = <span style="color: #B452CD">0.3</span>, learning_rate = <span style="color: #B452CD">0.1</span>,
-                max_depth = maxdegree, alpha = <span style="color: #B452CD">10</span>, n_estimators = <span style="color: #B452CD">10</span>)
+    model =  xgb.XGBRegressor(objective =<span style="color: #CD5555">&#39;reg:squarederror&#39;</span>, colsample_bytree = <span style="color: #B452CD">0.3</span>, learning_rate = <span style="color: #B452CD">0.1</span>,
+                max_depth = degree, alpha = <span style="color: #B452CD">10</span>, n_estimators = <span style="color: #B452CD">10</span>)
     model.fit(X_train_scaled,y_train)
     y_pred = model.predict(X_test_scaled)
     polydegree[degree] = degree
@@ -2464,6 +2463,7 @@ <h2 id="___sec55">Regression Case </h2>
     <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
     <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
 
+plt.xlim(<span style="color: #B452CD">1</span>,maxdegree-<span style="color: #B452CD">1</span>)
 plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;Error&#39;</span>)
 plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
 plt.plot(polydegree, variance, label=<span style="color: #CD5555">&#39;Variance&#39;</span>)

doc/pub/DecisionTrees/html/._DecisionTrees-bs055.html

@@ -2410,9 +2410,8 @@ <h2 id="___sec55">Regression Case </h2>
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">scikitplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">skplt</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.metrics</span> <span style="color: #8B008B; font-weight: bold">import</span> mean_squared_error
 
-n = <span style="color: #B452CD">40</span>
-n_boostraps = <span style="color: #B452CD">100</span>
-maxdegree = <span style="color: #B452CD">8</span>
+n = <span style="color: #B452CD">100</span>
+maxdegree = <span style="color: #B452CD">6</span>
 
 <span style="color: #228B22"># Make data set.</span>
 x = np.linspace(-<span style="color: #B452CD">3</span>, <span style="color: #B452CD">3</span>, n).reshape(-<span style="color: #B452CD">1</span>, <span style="color: #B452CD">1</span>)
@@ -2429,8 +2428,8 @@ <h2 id="___sec55">Regression Case </h2>
 X_test_scaled = scaler.transform(X_test)
 
 <span style="color: #8B008B; font-weight: bold">for</span> degree <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(maxdegree):
-    model =  xgb.XGBRegressor(objective =<span style="color: #CD5555">&#39;reg:linear&#39;</span>, colsample_bytree = <span style="color: #B452CD">0.3</span>, learning_rate = <span style="color: #B452CD">0.1</span>,
-                max_depth = maxdegree, alpha = <span style="color: #B452CD">10</span>, n_estimators = <span style="color: #B452CD">10</span>)
+    model =  xgb.XGBRegressor(objective =<span style="color: #CD5555">&#39;reg:squarederror&#39;</span>, colsample_bytree = <span style="color: #B452CD">0.3</span>, learning_rate = <span style="color: #B452CD">0.1</span>,
+                max_depth = degree, alpha = <span style="color: #B452CD">10</span>, n_estimators = <span style="color: #B452CD">10</span>)
     model.fit(X_train_scaled,y_train)
     y_pred = model.predict(X_test_scaled)
     polydegree[degree] = degree
@@ -2443,6 +2442,7 @@ <h2 id="___sec55">Regression Case </h2>
     <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;Var:&#39;</span>, variance[degree])
     <span style="color: #8B008B; font-weight: bold">print</span>(<span style="color: #CD5555">&#39;{} &gt;= {} + {} = {}&#39;</span>.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
 
+plt.xlim(<span style="color: #B452CD">1</span>,maxdegree-<span style="color: #B452CD">1</span>)
 plt.plot(polydegree, error, label=<span style="color: #CD5555">&#39;Error&#39;</span>)
 plt.plot(polydegree, bias, label=<span style="color: #CD5555">&#39;bias&#39;</span>)
 plt.plot(polydegree, variance, label=<span style="color: #CD5555">&#39;Variance&#39;</span>)

doc/pub/DecisionTrees/html/._DecisionTrees-bs056.html

@@ -2415,9 +2415,8 @@ <h2 id="___sec55">Regression Case </h2>
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">scikitplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">skplt</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> mean_squared_error
 
-n <span style="color: #666666">=</span> <span style="color: #666666">40</span>
-n_boostraps <span style="color: #666666">=</span> <span style="color: #666666">100</span>
-maxdegree <span style="color: #666666">=</span> <span style="color: #666666">8</span>
+n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
+maxdegree <span style="color: #666666">=</span> <span style="color: #666666">6</span>
 
 <span style="color: #408080; font-style: italic"># Make data set.</span>
 x <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(<span style="color: #666666">-3</span>, <span style="color: #666666">3</span>, n)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
@@ -2434,8 +2433,8 @@ <h2 id="___sec55">Regression Case </h2>
 X_test_scaled <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>transform(X_test)
 
 <span style="color: #008000; font-weight: bold">for</span> degree <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(maxdegree):
-    model <span style="color: #666666">=</span>  xgb<span style="color: #666666">.</span>XGBRegressor(objective <span style="color: #666666">=</span><span style="color: #BA2121">&#39;reg:linear&#39;</span>, colsample_bytree <span style="color: #666666">=</span> <span style="color: #666666">0.3</span>, learning_rate <span style="color: #666666">=</span> <span style="color: #666666">0.1</span>,
-                max_depth <span style="color: #666666">=</span> maxdegree, alpha <span style="color: #666666">=</span> <span style="color: #666666">10</span>, n_estimators <span style="color: #666666">=</span> <span style="color: #666666">10</span>)
+    model <span style="color: #666666">=</span>  xgb<span style="color: #666666">.</span>XGBRegressor(objective <span style="color: #666666">=</span><span style="color: #BA2121">&#39;reg:squarederror&#39;</span>, colsample_bytree <span style="color: #666666">=</span> <span style="color: #666666">0.3</span>, learning_rate <span style="color: #666666">=</span> <span style="color: #666666">0.1</span>,
+                max_depth <span style="color: #666666">=</span> degree, alpha <span style="color: #666666">=</span> <span style="color: #666666">10</span>, n_estimators <span style="color: #666666">=</span> <span style="color: #666666">10</span>)
     model<span style="color: #666666">.</span>fit(X_train_scaled,y_train)
     y_pred <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(X_test_scaled)
     polydegree[degree] <span style="color: #666666">=</span> degree
@@ -2448,6 +2447,7 @@ <h2 id="___sec55">Regression Case </h2>
     <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;Var:&#39;</span>, variance[degree])
     <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&#39;{} &gt;= {} + {} = {}&#39;</span><span style="color: #666666">.</span>format(error[degree], bias[degree], variance[degree], bias[degree]<span style="color: #666666">+</span>variance[degree]))
 
+plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">1</span>,maxdegree<span style="color: #666666">-1</span>)
 plt<span style="color: #666666">.</span>plot(polydegree, error, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;Error&#39;</span>)
 plt<span style="color: #666666">.</span>plot(polydegree, bias, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bias&#39;</span>)
 plt<span style="color: #666666">.</span>plot(polydegree, variance, label<span style="color: #666666">=</span><span style="color: #BA2121">&#39;Variance&#39;</span>)

doc/pub/DecisionTrees/html/._DecisionTrees-bs057.html

@@ -2509,9 +2509,8 @@
     "import scikitplot as skplt\n",
     "from sklearn.metrics import mean_squared_error\n",
     "\n",
-    "n = 40\n",
-    "n_boostraps = 100\n",
-    "maxdegree = 8\n",
+    "n = 100\n",
+    "maxdegree = 6\n",
     "\n",
     "# Make data set.\n",
     "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
@@ -2528,8 +2527,8 @@
     "X_test_scaled = scaler.transform(X_test)\n",
     "\n",
     "for degree in range(maxdegree):\n",
-    "    model =  xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
-    "                max_depth = maxdegree, alpha = 10, n_estimators = 10)\n",
+    "    model =  xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n",
+    "                max_depth = degree, alpha = 10, n_estimators = 10)\n",
     "    model.fit(X_train_scaled,y_train)\n",
     "    y_pred = model.predict(X_test_scaled)\n",
     "    polydegree[degree] = degree\n",
@@ -2542,6 +2541,7 @@
     "    print('Var:', variance[degree])\n",
     "    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
     "\n",
+    "plt.xlim(1,maxdegree-1)\n",
     "plt.plot(polydegree, error, label='Error')\n",
     "plt.plot(polydegree, bias, label='bias')\n",
     "plt.plot(polydegree, variance, label='Variance')\n",