{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"![baner aitech](../grafiki/SE7/logo.jpg \"baner\") \n",
"\n",
"***\n",
"\n",
"# Sztuczna empatia 7\n",
"## Empatia w medycynie\n",
"### Joanna Siwek\n",
"\n",
"***\n",
"\n",
"### Spis treści\n",
"1. [Wstęp](#introduction)\n",
"2. [Uszkodzenia mózgu a empatia](#paragraph1)\n",
"3. [Zastosowania SE w medycynie](#paragraph2)\n",
"4. [Zastosowania SE w psychologii](#paragraph3)\n",
"5. [Zadania](#paragraph4)\n",
"\n",
"***\n",
"\n",
"![baner ue](../grafiki/SE7/ae_ue.jpg \"baner\") "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wstęp \n",
"
\n",
"
\n", " | name | \n", "MDVP:Fo(Hz) | \n", "MDVP:Fhi(Hz) | \n", "MDVP:Flo(Hz) | \n", "MDVP:Jitter(%) | \n", "MDVP:Jitter(Abs) | \n", "MDVP:RAP | \n", "MDVP:PPQ | \n", "Jitter:DDP | \n", "MDVP:Shimmer | \n", "... | \n", "Shimmer:DDA | \n", "NHR | \n", "HNR | \n", "status | \n", "RPDE | \n", "DFA | \n", "spread1 | \n", "spread2 | \n", "D2 | \n", "PPE | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "phon_R01_S01_1 | \n", "119.992 | \n", "157.302 | \n", "74.997 | \n", "0.00784 | \n", "0.00007 | \n", "0.00370 | \n", "0.00554 | \n", "0.01109 | \n", "0.04374 | \n", "... | \n", "0.06545 | \n", "0.02211 | \n", "21.033 | \n", "1 | \n", "0.414783 | \n", "0.815285 | \n", "-4.813031 | \n", "0.266482 | \n", "2.301442 | \n", "0.284654 | \n", "
1 | \n", "phon_R01_S01_2 | \n", "122.400 | \n", "148.650 | \n", "113.819 | \n", "0.00968 | \n", "0.00008 | \n", "0.00465 | \n", "0.00696 | \n", "0.01394 | \n", "0.06134 | \n", "... | \n", "0.09403 | \n", "0.01929 | \n", "19.085 | \n", "1 | \n", "0.458359 | \n", "0.819521 | \n", "-4.075192 | \n", "0.335590 | \n", "2.486855 | \n", "0.368674 | \n", "
2 | \n", "phon_R01_S01_3 | \n", "116.682 | \n", "131.111 | \n", "111.555 | \n", "0.01050 | \n", "0.00009 | \n", "0.00544 | \n", "0.00781 | \n", "0.01633 | \n", "0.05233 | \n", "... | \n", "0.08270 | \n", "0.01309 | \n", "20.651 | \n", "1 | \n", "0.429895 | \n", "0.825288 | \n", "-4.443179 | \n", "0.311173 | \n", "2.342259 | \n", "0.332634 | \n", "
3 | \n", "phon_R01_S01_4 | \n", "116.676 | \n", "137.871 | \n", "111.366 | \n", "0.00997 | \n", "0.00009 | \n", "0.00502 | \n", "0.00698 | \n", "0.01505 | \n", "0.05492 | \n", "... | \n", "0.08771 | \n", "0.01353 | \n", "20.644 | \n", "1 | \n", "0.434969 | \n", "0.819235 | \n", "-4.117501 | \n", "0.334147 | \n", "2.405554 | \n", "0.368975 | \n", "
4 | \n", "phon_R01_S01_5 | \n", "116.014 | \n", "141.781 | \n", "110.655 | \n", "0.01284 | \n", "0.00011 | \n", "0.00655 | \n", "0.00908 | \n", "0.01966 | \n", "0.06425 | \n", "... | \n", "0.10470 | \n", "0.01767 | \n", "19.649 | \n", "1 | \n", "0.417356 | \n", "0.823484 | \n", "-3.747787 | \n", "0.234513 | \n", "2.332180 | \n", "0.410335 | \n", "
5 rows × 24 columns
\n", "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", " colsample_bylevel=None, colsample_bynode=None,\n", " colsample_bytree=None, early_stopping_rounds=None,\n", " enable_categorical=False, eval_metric=None, feature_types=None,\n", " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", " interaction_constraints=None, learning_rate=None, max_bin=None,\n", " max_cat_threshold=None, max_cat_to_onehot=None,\n", " max_delta_step=None, max_depth=None, max_leaves=None,\n", " min_child_weight=None, missing=nan, monotone_constraints=None,\n", " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n", " predictor=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
XGBClassifier(base_score=None, booster=None, callbacks=None,\n", " colsample_bylevel=None, colsample_bynode=None,\n", " colsample_bytree=None, early_stopping_rounds=None,\n", " enable_categorical=False, eval_metric=None, feature_types=None,\n", " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", " interaction_constraints=None, learning_rate=None, max_bin=None,\n", " max_cat_threshold=None, max_cat_to_onehot=None,\n", " max_delta_step=None, max_depth=None, max_leaves=None,\n", " min_child_weight=None, missing=nan, monotone_constraints=None,\n", " n_estimators=100, n_jobs=None, num_parallel_tree=None,\n", " predictor=None, random_state=None, ...)