diff --git a/notebooks/least_core_basic.ipynb b/notebooks/least_core_basic.ipynb index 00aa46636..4ea41cc67 100644 --- a/notebooks/least_core_basic.ipynb +++ b/notebooks/least_core_basic.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "id": "08ee61fd", "metadata": { "tags": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "id": "3155c98e", "metadata": {}, "outputs": [], @@ -163,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "id": "2e916a67", "metadata": {}, "outputs": [], @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "id": "4b128695", "metadata": {}, "outputs": [], @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "id": "ffce8661", "metadata": {}, "outputs": [], @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "id": "270c1bf3", "metadata": {}, "outputs": [ @@ -216,8 +216,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Training accuracy: 100.00%\n", - "Testing accuracy: 25.00%\n" + "Training accuracy: 86.25%\n", + "Testing accuracy: 70.00%\n" ] } ], @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "id": "e539e5ed", "metadata": {}, "outputs": [ @@ -242,7 +242,7 @@ "output_type": "stream", "text": [ "Training accuracy: 100.00%\n", - "Testing accuracy: 37.50%\n" + "Testing accuracy: 47.89%\n" ] } ], @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "id": "98a22915", "metadata": {}, "outputs": [], @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "id": "aa91a124", "metadata": { "editable": true, @@ -293,12 +293,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea155a8e0b5844b28a0fca64da2b7ca0", + "model_id": "4aef7143d90345c3abcbc1cc6a731af9", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/15 [00:00" ] @@ -443,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "48bccf93", "metadata": { "editable": true, @@ -455,7 +455,20 @@ "invertible-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "mean_std_values_df = values_df.drop(columns=\"budget\").agg([\"mean\", \"std\"])\n", "df = pd.concat([exact_values_df, mean_std_values_df])\n", @@ -489,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "019b6c45", "metadata": {}, "outputs": [], @@ -499,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "85a0a9d2", "metadata": {}, "outputs": [], @@ -520,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "985c27e0", "metadata": { "editable": true, @@ -531,7 +544,22 @@ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d89e8aac1ce4453f9e4d097bee9d8d2e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/5 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "\n", @@ -614,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "6f33b5bb", "metadata": { "editable": true, @@ -625,7 +666,22 @@ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "241aded4b743402b962d09d78df0705f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/5 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "\n",