Monte Carlo Cross-Validation(Shuffle Split).ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "50c4fc40",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0757957d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit,cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2e7f60f",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=load_iris()\n",
    "X=iris.data\n",
    "Y=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2aa1721",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg=LogisticRegression()\n",
    "shuffle_split=ShuffleSplit(test_size=0.3,train_size=0.5,n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "859a0bfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(logreg,X,Y,cv=shuffle_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1c02db5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross Validation scores:n [0.95555556 0.93333333 0.93333333 0.93333333 0.93333333 0.95555556\n",
      " 0.97777778 0.97777778 0.93333333 0.95555556]\n",
      "Average Cross Validation score :0.9488888888888889\n"
     ]
    }
   ],
   "source": [
    "print(\"cross Validation scores:n {}\".format(scores))\n",
    "print(\"Average Cross Validation score :{}\".format(scores.mean()))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}