diff --git a/wyk/03_Regresja_logistyczna.ipynb b/wyk/03_Regresja_logistyczna.ipynb index fb2dab0..d649822 100644 --- a/wyk/03_Regresja_logistyczna.ipynb +++ b/wyk/03_Regresja_logistyczna.ipynb @@ -56,7 +56,7 @@ "source": [ "\n", "\n", - "### Przykład: kosaciec szczecinkowy (_Iris setosa_)\n", + "### Przykład: kosaciec szczecinkowy (*Iris setosa*)\n", "\n", "Mamy dane dotyczące długości płatków i chcielibyśmy na tej podstawie określić, czy jest to roślina z gatunku _Iris setosa_" ] @@ -10188,7 +10188,7 @@ "source": [ "\n", "\n", - "Kosaciec szczecinkowy (_Iris setosa_)" + "Kosaciec szczecinkowy (*Iris setosa*)" ] }, { @@ -10201,7 +10201,7 @@ "source": [ "\n", "\n", - "Kosaciec amerykański (_Iris virginica_)" + "Kosaciec amerykański (*Iris virginica*)" ] }, { @@ -10214,7 +10214,7 @@ "source": [ "\n", "\n", - "Kosaciec różnobarwny (_Iris versicolor_)" + "Kosaciec różnobarwny (*Iris versicolor*)" ] }, { diff --git a/wyk/04_Metody_ewaluacji.ipynb b/wyk/04_Metody_ewaluacji.ipynb index b4532a4..d4bc252 100644 --- a/wyk/04_Metody_ewaluacji.ipynb +++ b/wyk/04_Metody_ewaluacji.ipynb @@ -8,8 +8,7 @@ } }, "source": [ - "## Uczenie maszynowe UMZ 2018/2019\n", - "### 31 marca 2020\n", + "## Uczenie maszynowe – zastosowania\n", "# 4. Metody ewaluacji" ] }, @@ -177,7 +176,7 @@ "source": [ "### Walidacja krzyżowa – wskazówki\n", "\n", - "* Zazwyczaj ustala się $N$ w przedziale od $4$ do $10$, tzw. $N$-krotna walidacja krzyżowa (_$N$-fold cross validation_). \n", + "* Zazwyczaj ustala się $N$ w przedziale od $4$ do $10$, tzw. $N$-krotna walidacja krzyżowa (*$N$-fold cross validation*). \n", "* Zbiór $D$ warto zrandomizować przed podziałem.\n", "* W jaki sposób akumulować wyniki dla wszystkich zbiórow $T_i$?\n", "* Po ustaleniu parametrów dla każdego $T_i$, trenujemy model na całych danych treningowych z ustalonymi parametrami.\n", @@ -221,7 +220,7 @@ "### Zbiór walidujący a algorytmy optymalizacji\n", "\n", "* Gdy błąd rośnie na zbiorze uczącym, mamy źle dobrany parametr $\\alpha$. Należy go wtedy zmniejszyć.\n", - "* Gdy błąd zmniejsza się na zbiorze trenującym, ale rośnie na zbiorze walidującym, mamy do czynienia ze zjawiskiem **nadmiernego dopasowania** (_overfitting_).\n", + "* Gdy błąd zmniejsza się na zbiorze trenującym, ale rośnie na zbiorze walidującym, mamy do czynienia ze zjawiskiem **nadmiernego dopasowania** (*overfitting*).\n", "* Należy wtedy przerwać optymalizację. Automatyzacja tego procesu to _early stopping_." ] }, @@ -440,7 +439,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -625,24 +624,18 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/pawel/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: The following kwargs were not used by contour: 'lw'\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - }, { "data": { - "image/png": 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\n", + "application/vnd.jupyter.widget-view+json": { + "model_id": "d6c3033020f54951a9a8c766ef776d17", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "
" + "interactive(children=(Dropdown(description='highlight', options=('all', 'tp', 'fp', 'tn', 'fn'), value='all'),…" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -712,7 +705,11 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "outputs": [ { "name": "stdout", @@ -751,7 +748,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, "source": [ "Możemy teraz zdefiniować następujące metryki:" ] @@ -764,7 +765,7 @@ } }, "source": [ - "#### Dokładność (_accuracy_)\n", + "#### Dokładność (*accuracy*)\n", "$$ \\mbox{accuracy} = \\frac{\\mbox{przypadki poprawnie sklasyfikowane}}{\\mbox{wszystkie przypadki}} = \\frac{TP + TN}{TP + TN + FP + FN} $$" ] }, @@ -830,7 +831,7 @@ } }, "source": [ - "#### Precyzja (_precision_)\n", + "#### Precyzja (*precision*)\n", "$$ \\mbox{precision} = \\frac{TP}{TP + FP} $$" ] }, @@ -875,7 +876,7 @@ } }, "source": [ - "#### Pokrycie (czułość, _recall_)\n", + "#### Pokrycie (czułość, *recall*)\n", "$$ \\mbox{recall} = \\frac{TP}{TP + FN} $$" ] }, @@ -920,7 +921,7 @@ } }, "source": [ - "#### _$F$-measure_ (_$F$-score_)\n", + "#### *$F$-measure* (*$F$-score*)\n", "$$ F = \\frac{2 \\cdot \\mbox{precision} \\cdot \\mbox{recall}}{\\mbox{precision} + \\mbox{recall}} $$" ] }, @@ -976,7 +977,7 @@ } }, "source": [ - "_$F_\\beta$-measure_:\n", + "*$F_\\beta$-measure*:\n", "$$ F_\\beta = \\frac{(1 + \\beta) \\cdot \\mbox{precision} \\cdot \\mbox{recall}}{\\beta^2 \\cdot \\mbox{precision} + \\mbox{recall}} $$" ] }, @@ -1020,7 +1021,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, "source": [ "W poniższym przykładzie można zobaczyć wpływ obserwacji odstających na wynik modelowania na przykładzie danych dotyczących cen mieszkań zebranych z ogłoszeń na portalu Gratka.pl: tutaj przykładem obserwacji odstającej może być ogłoszenie, w którym podano cenę w tys. zł zamiast ceny w zł." ] @@ -1136,7 +1141,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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\n", 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\n", 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" ] @@ -1239,7 +1244,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, "source": [ "Na powyższym wykresie widać, że po odrzuceniu obserwacji odstających otrzymujemy dużo bardziej „wiarygodną” krzywą regresji." ] @@ -1266,7 +1275,7 @@ }, "livereveal": { "start_slideshow_at": "selected", - "theme": "amu" + "theme": "white" } }, "nbformat": 4,