Normalisierung und Erstellung von Train/Test

0-pilot-project/MNIST-template.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7a0c752a",
+   "id": "6904e7ae",
    "metadata": {},
    "source": [
     "### Load MNIST dataset"
@@ -11,7 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e07d82fe",
+   "id": "e3d41c8f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "1f97dcb1",
+   "id": "55990ccc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "01f83832",
+   "id": "933f52fb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "affa0e2b",
+   "id": "41435175",
    "metadata": {},
    "outputs": [
     {
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d51fd43",
+   "id": "fe14558d",
    "metadata": {},
    "source": [
     "Bunch objects are sometimes used as an output for functions and methods. They extend dictionaries by enabling values to be accessed by key, bunch[\"value_key\"], or by an attribute, bunch.value_key.\\\n",
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "78be57ab",
+   "id": "80a39a2e",
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d0450c41",
+   "id": "5c211b9c",
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e61e2adb",
+   "id": "a4c5fb8d",
    "metadata": {},
    "source": [
     "Datasets loaded by Scikit-Learn generally have a similar dictionary structure, including the following:\\\n",
@@ -139,7 +139,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "fe285433",
+   "id": "7c077427",
    "metadata": {},
    "outputs": [
     {
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a70a746",
+   "id": "f3f2e42a",
    "metadata": {},
    "source": [
     "### Prepare the MNIST dataset"
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9b7a120",
+   "id": "6d5d2658",
    "metadata": {},
    "source": [
     "$f(X) = y$\n",
@@ -181,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4e02cf2a",
+   "id": "99784cec",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "001d736f",
+   "id": "923676c7",
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +212,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b344be1d",
+   "id": "ed44fc7a",
    "metadata": {},
    "outputs": [
     {
@@ -233,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "cef23e9f",
+   "id": "94ee3e59",
    "metadata": {},
    "outputs": [
     {
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe3b1259",
+   "id": "478cb336",
    "metadata": {},
    "source": [
     "### Plot data"
@@ -262,7 +262,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "953d9415",
+   "id": "f3cfebc6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "b68f6cee",
+   "id": "6d799c25",
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "8779b1a2",
+   "id": "72c7305b",
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +317,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "dcc605cf",
+   "id": "f5b0b349",
    "metadata": {},
    "outputs": [
     {
@@ -343,7 +343,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "6d41d752",
+   "id": "c70641f8",
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "230cfd35",
+   "id": "98f8561b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +375,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "25a3a2e7",
+   "id": "33e244b2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,7 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "f1552762",
+   "id": "20043b74",
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +413,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "74b3a063",
+   "id": "6e4369de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "949b3914",
+   "id": "fb3f6d95",
    "metadata": {},
    "outputs": [
     {
@@ -454,7 +454,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec8a9d34",
+   "id": "3a69d0fd",
    "metadata": {},
    "source": [
     "### Prepare data for machine learning"
@@ -463,7 +463,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "febbd286",
+   "id": "dcc31672",
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +485,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "fff839b6",
+   "id": "282ba914",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bdbeb4e",
+   "id": "5c34bbd5",
    "metadata": {},
    "source": [
     "### Train classifier"
@@ -504,7 +504,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "4c32ae9f",
+   "id": "f0b6e1c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -515,7 +515,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "fe06ae55",
+   "id": "54e3fb64",
    "metadata": {},
    "outputs": [
     {
@@ -540,7 +540,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "e6209258",
+   "id": "29446c32",
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "62773b1b",
+   "id": "5030ccc3",
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +584,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "0ce21474",
+   "id": "22c780fb",
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +603,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "78a8e8a7",
+   "id": "990d3925",
    "metadata": {},
    "outputs": [
     {
@@ -626,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "45d93a99",
+   "id": "b6e1d70c",
    "metadata": {},
    "outputs": [
     {
@@ -647,7 +647,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc739051",
+   "id": "a65ed630",
    "metadata": {},
    "source": [
     "### Evaluation"
@@ -656,7 +656,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "990a5b7c",
+   "id": "4c93c5f9",
    "metadata": {},
    "outputs": [
     {
@@ -677,7 +677,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "f125a37d",
+   "id": "f01ec3dd",
    "metadata": {},
    "outputs": [
     {
@@ -697,7 +697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdcb6e6e",
+   "id": "c41db6b0",
    "metadata": {},
    "source": [
     "Accuracy is strongly influenced by the distribution of the classes in the test data."
@@ -705,7 +705,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be858cd5",
+   "id": "3f830f36",
    "metadata": {},
    "source": [
     "#### Cross Validation\n",
@@ -715,7 +715,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "7adb1ea7",
+   "id": "eeec5311",
    "metadata": {},
    "outputs": [
     {
@@ -736,7 +736,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "11d22c5e",
+   "id": "d1ed46a3",
    "metadata": {},
    "outputs": [
     {
@@ -759,7 +759,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b54e83a5",
+   "id": "539cfa0c",
    "metadata": {},
    "source": [
     "#### Precision"
@@ -768,7 +768,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "ef7a9e7e",
+   "id": "abfe8383",
    "metadata": {},
    "outputs": [
     {
@@ -790,7 +790,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da723740",
+   "id": "d899dd6f",
    "metadata": {},
    "source": [
     "#### Recall"
@@ -799,7 +799,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "cb77bf58",
+   "id": "15d30ae5",
    "metadata": {},
    "outputs": [
     {
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28867d1b",
+   "id": "393c3b1c",
    "metadata": {},
    "source": [
     "#### F1 Score"
@@ -830,7 +830,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "0674e0de",
+   "id": "53fa1823",
    "metadata": {},
    "outputs": [
     {
@@ -852,7 +852,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da59da11",
+   "id": "08b6bdc2",
    "metadata": {},
    "source": [
     "#### Confusion Matrix"
@@ -861,7 +861,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "adbdeece",
+   "id": "e205d359",
    "metadata": {},
    "outputs": [
     {
@@ -891,7 +891,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "fb50c5a4",
+   "id": "4ec777ac",
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +929,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "2f0d536a",
+   "id": "9c53a0a7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -940,7 +940,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "dddf5fe8",
+   "id": "c47e7c69",
    "metadata": {},
    "outputs": [
     {
@@ -970,7 +970,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "44537aae",
+   "id": "ef09fc40",
    "metadata": {},
    "outputs": [
     {
@@ -1006,7 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57d96f56",
+   "id": "6f9816f1",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1028,7 +1028,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   }
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  "nbformat": 4,