How to use k fold cross validation in sklearn

sklearn_exercise_07.ipynb

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    "# How to use k-fold cross validation in sklearn?\n",
    "1. K-Folds cross-validator\n",
    "2. The k-fold cross-validation procedure is a method for estimating the performance of a ML algorithm on a dataset. The k-fold cross-validation procedure divides a limited dataset into k non-overlapping folds.  A total of k models are fit and evaluated on the k hold-out test sets and the mean performance is reported.\n",
    "3. Each fold is then used once as a validation while the k - 1 remaining folds form the training set."
   ]
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   "source": [
    "## Step:1 Import Libraries:-"
   ]
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the range 1 to 25\n",
    "rn = range(1,26)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Step:2 Createing Folds"
   ]
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   "source": [
    "# to demonstrate how the data are split, we will create 3 and 5 folds. \n",
    "# it returns an location (index) of the train and test samples.\n",
    "kf5 = KFold(n_splits=5, shuffle=False)\n",
    "kf3 = KFold(n_splits=3, shuffle=False)"
   ]
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     "text": [
      "[ 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [0 1 2 3 4 5 6 7 8]\n",
      "[ 0  1  2  3  4  5  6  7  8 17 18 19 20 21 22 23 24] [ 9 10 11 12 13 14 15 16]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16] [17 18 19 20 21 22 23 24]\n"
     ]
    }
   ],
   "source": [
    "# the Kfold function retunrs the indices of the data. Our range goes from 1-25 so the index is 0-24\n",
    "for train_index, test_index in kf3.split(rn):\n",
    "    print(train_index, test_index)"
   ]
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   "cell_type": "code",
   "execution_count": 5,
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     "text": [
      "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25] [1 2 3 4 5 6 7 8 9]\n",
      "[ 1  2  3  4  5  6  7  8  9 18 19 20 21 22 23 24 25] [10 11 12 13 14 15 16 17]\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17] [18 19 20 21 22 23 24 25]\n"
     ]
    }
   ],
   "source": [
    "# to get the values from our data, we use np.take() to access a value at particular index\n",
    "for train_index, test_index in kf3.split(rn):\n",
    "    print(np.take(rn,train_index), np.take(rn,test_index))"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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