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z3xTt+|2`Jr4#-0F&wfzAIR2{+xTXBQHYhtB{<{pIaOi*W3`m9TcmAMocJP0XWoP+a z_JBFC|2~!(3}OE7eo*$``C|s(d~EpV+L*yCu>bDI3VgKqy&oL%yZo3TU}nJUZyq=H zVr!!dWZbg3x#26AI2iz@#w2fL1>A7{%HYH#ZfR%*;E``{h)m)_APo?ghyXi_Ae$gu zm|c*KAIi@R6Jq9P6%d37um}mW!QjF?DF1hto83joN>9+(K+o)rodt+VfSp|gc(4mW wnFXLwhycHc5Hk!S0EG(!KZ~)$SZ}t3H@3Pqwzn<;WFBK@Mxme(mJvbue<)TWtpET3 diff --git a/dane_z_moodle.txt b/dane_z_moodle.txt deleted file mode 100644 index 781e9c7..0000000 --- a/dane_z_moodle.txt +++ /dev/null @@ -1,21 +0,0 @@ -Projekt realizowany jest indywidualnie. - -Projekt polega na zastosowaniu poznanych metod uczenia maszynowego do znalezienia rozwiązania wybranego problemu. - -Oprócz zbudowania odpowiedniego modelu, oceniane będzie odpowiednie przygotowanie danych uczących oraz ewaluacja stworzonego modelu. - -Dodatkowo, żeby otrzymać maksymalną liczbę punktów, przynajmniej jeden z modeli powinien być siecią neuronową. - -Pliki potrzebne do uruchomienia projektu (kod i dane) należy dołączyć na Moodle'u lub umieścić w repozytorium (wówczas wystarczy podać jego adres). - -Do oceny projektu proszę przygotować również krótki raport, zawierający: - - cel projektu / definicję problemu (jakie zagadnienie Państwo rozwiązywali) - opis użytych danych (skąd pochodzą dane, ile przykładów zawierają zbiory uczący i testowy) - opis wykorzystanych metod (jakich modeli Państwo użyli) - tabelkę z wynikami ewaluacji - ewentualne wnioski - -Wzór raportu znajduje się w pliku Wzór raportu.docx - -Maksymalna liczba punktów: 34 \ No newline at end of file diff --git a/mushrooms.ipynb b/mushrooms.ipynb index 27d5d54..4cda4ec 100644 --- a/mushrooms.ipynb +++ b/mushrooms.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 127, + "execution_count": 165, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 167, "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 168, "metadata": {}, "outputs": [ { @@ -105,7 +105,7 @@ "dtype: int64" ] }, - "execution_count": 130, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 169, "metadata": {}, "outputs": [ { @@ -147,7 +147,7 @@ "dtype: int64" ] }, - "execution_count": 131, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -505,7 +505,7 @@ "[792 rows x 117 columns]" ] }, - "execution_count": 132, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -867,7 +867,7 @@ "[6465 rows x 118 columns]" ] }, - "execution_count": 133, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -880,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 172, "metadata": {}, "outputs": [ { @@ -904,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 173, "metadata": {}, "outputs": [ { @@ -1251,7 +1251,7 @@ "[792 rows x 117 columns]" ] }, - "execution_count": 135, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -1263,7 +1263,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -1288,15 +1288,15 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 175, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.96\n", - "0.96\n" + "0.95\n", + "0.97\n" ] } ], @@ -1312,7 +1312,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -1335,7 +1335,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 177, "metadata": {}, "outputs": [ { @@ -1373,7 +1373,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -1396,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -1411,8 +1411,8 @@ " trujące 0.84 1.00 0.91 386\n", "\n", " accuracy 0.91 792\n", - " macro avg 0.92 0.91 0.90 792\n", - "weighted avg 0.92 0.91 0.90 792\n", + " macro avg 0.92 0.91 0.91 792\n", + "weighted avg 0.92 0.91 0.91 792\n", "\n" ] } @@ -1428,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -1458,7 +1458,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -1488,7 +1488,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 182, "metadata": {}, "outputs": [ { @@ -1526,7 +1526,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 183, "metadata": {}, "outputs": [ {