From 44ae9e8b1158431f02888a2a3aa3b11b2b279f73 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= <pawel.skorzewski@amu.edu.pl>
Date: Fri, 25 Mar 2022 10:08:25 +0100
Subject: [PATCH] =?UTF-8?q?Usuwanie=20obserwacji=20odstaj=C4=85cych?=
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---
 wyk/04_Metody_ewaluacji.ipynb | 37 +++++++++++++++++++----------------
 1 file changed, 20 insertions(+), 17 deletions(-)

diff --git a/wyk/04_Metody_ewaluacji.ipynb b/wyk/04_Metody_ewaluacji.ipynb
index b3160a7..117f543 100644
--- a/wyk/04_Metody_ewaluacji.ipynb
+++ b/wyk/04_Metody_ewaluacji.ipynb
@@ -261,7 +261,9 @@
     "### Metryki dla zadań regresji\n",
     "\n",
     "Dla zadań regresji możemy zastosować np.:\n",
-    " * błąd średniokwadratowy (*root-mean-square error*, RMSE):\n",
+    " * błąd średniokwadratowy (*mean-square error*, MSE):\n",
+    "   $$ \\mathrm{MSE} \\, = \\, \\frac{1}{m} \\sum_{i=1}^{m} \\left( \\hat{y}^{(i)} - y^{(i)} \\right)^2 $$\n",
+    " * pierwiastek z błędu średniokwadratowego (*root-mean-square error*, RMSE):\n",
     "   $$ \\mathrm{RMSE} \\, = \\, \\sqrt{ \\frac{1}{m} \\sum_{i=1}^{m} \\left( \\hat{y}^{(i)} - y^{(i)} \\right)^2 } $$\n",
     " * średni błąd bezwzględny (*mean absolute error*, MAE):\n",
     "   $$ \\mathrm{MAE} \\, = \\, \\frac{1}{m} \\sum_{i=1}^{m} \\left| \\hat{y}^{(i)} - y^{(i)} \\right| $$"
@@ -293,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -315,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -334,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -406,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -430,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -456,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -503,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -531,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -561,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -583,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -597,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -617,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -627,7 +629,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "32929ab5e3024128bd39a6c165e50196",
+       "model_id": "6325cec10a034a9d96d862dee900013d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -644,7 +646,7 @@
        "<function __main__.interactive_classification(highlight)>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1185,9 +1187,10 @@
     "# Odrzućmy obserwacje odstające\n",
     "alldata_no_outliers = [\n",
     "    (index, item) for index, item in alldata.iterrows() \n",
-    "    if item.price > 100 and item.sqrMetres > 10]\n",
+    "    if item.price > 10000 and item.sqrMetres < 1000]\n",
     "\n",
-    "alldata_no_outliers = alldata.loc[(alldata['price'] > 100) & (alldata['sqrMetres'] > 100)]"
+    "# Alternatywnie można to zrobić w następujący sposób\n",
+    "alldata_no_outliers = alldata.loc[(alldata['price'] > 10000) & (alldata['sqrMetres'] < 1000)]"
    ]
   },
   {