refactor: move things around
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@@ -0,0 +1,216 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [],
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"toc": true
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},
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"source": [
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"# WS 15 Schwellenwert für Accuracy"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* `.predict_proba()` gibt bei allen Klassifikatoren die Wahrscheinlichkeit für die Zugehörigkeit zu den einlenen Klassen zurück, `predict()` dagegen die wahrscheinlichtste Klasse selber\n",
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"* für Zwei-Klassen Fragestellungen bedeutet dies, dass bei einer Wahrscheinlickkeit (proba) `> 0.5` für die erste Klasse diese zurückgegeben wird, andernfalls die zweite Klasse, `0.5` ist somit ein scheinbar willkürlicher Schwellenwert\n",
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"* untersuchen Sie die Auswirkung anderer Schwellenwerte auf die Accuracy mit `RandomForestClassifier` auf den aufbereiteten Bankkunden-Datan\n",
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"\n",
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"* vorgeschlagenes Vorgehen:\n",
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" * trainieren eines RandomForestClassifier mit den vorbereiteten Bankkundendaten (Trainingsdaten)\n",
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" * bestimmen der Wahrscheinlichkeit für jede Beobachtung der entsprechenden Testdaten zur Klasse 'no'\n",
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" * erstellen einens Range der zu untersuchenden Schwellenwerte, z.B. mit np.arange()\n",
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" * in einem Loop über alle Werte dieses Ranges\n",
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" * `y_pred` für den jeweiligen Schwellenwert berechnen (wiederum als `['no', 'yes']`)\n",
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" * `accuracy_score()` der jeweiligen Prediction (und sammeln in einer Liste)\n",
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" * ausgeben des besten Score-Wertes und des zugehörigen Schwellenwertes in der Konsole\n",
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" * visualisieren der Ergebnisse auch als Lineplot "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"## prepare env, read and prepare data\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns; sns.set()\n",
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"\n",
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"codepath = '../2_code' ## for import of user defined module\n",
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"datapath = '../3_data'\n",
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"\n",
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"from sys import path; path.insert(1, codepath)\n",
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"from os import chdir; chdir(datapath)\n",
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"\n",
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"from bfh_cas_pml import prep_data\n",
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"X_train, X_test, y_train, y_test = prep_data('bank_data_prep.csv', target='y', seed=1234)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-15T21:03:01.918701Z",
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"start_time": "2020-04-15T21:03:01.844142Z"
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}
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},
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"outputs": [],
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"source": [
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"## train a model\n",
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"from sklearn.ensemble import RandomForestClassifier \n",
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"model = RandomForestClassifier(random_state=1234)\n",
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"model.fit(X_train, y_train) \n",
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"\n",
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"## prediction using .predict_proba()\n",
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"y_pred_p_no = model.predict_proba(X_test)[:, 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.0\n",
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"0.1\n",
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"0.2\n",
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"0.30000000000000004\n",
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"0.4\n",
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"0.5\n",
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"0.6000000000000001\n",
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"0.7000000000000001\n",
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"0.8\n",
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"0.9\n",
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"1.0\n"
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]
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}
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],
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"source": [
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"## inspect different threshold values\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"thresholds = np.arange(0, 1.01, 0.1) ## test over 10\n",
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"#thresholds = np.arange(0, 1.01, 0.01)\n",
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"\n",
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"scores = []\n",
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"\n",
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"for threshold in thresholds:\n",
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" ## tbd\n",
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" print(threshold)\n",
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"\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"## results\n",
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"## tbd\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"## viszalization\n",
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"## tbd\n",
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"\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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},
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"toc": {
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"base_numbering": "",
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "WS 15 Validierung - Sampling",
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"title_sidebar": "Contents",
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"toc_cell": true,
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"toc_position": {
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"height": "calc(100% - 180px)",
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"left": "10px",
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"top": "150px",
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"width": "195.867px"
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},
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"toc_section_display": true,
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"toc_window_display": true
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"position": {
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"height": "306.85px",
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"left": "862px",
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"right": "20px",
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"top": "137px",
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"width": "350px"
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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