Model-Validation.ipynb

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    "# Model Validation Methods"
   ]
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   "source": [
    "#### 1.Evaluate using a train and a test set"
   ]
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      "C:\\Users\\Hp\\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"
     ]
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   "source": [
    "# Evaluate using a train and a test set\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "filename = r'C:\\Users\\Hp\\OneDrive\\Documents\\ExcelR\\Python Intro\\Feature Engineering\\pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "test_size = 0.33\n",
    "seed = 7\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,\n",
    "random_state=seed)\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "result = model.score(X_test, Y_test)\n"
   ]
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   "source": [
    "#### 2.Evaluate using Cross Validation"
   ]
  },
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   "source": [
    "# Evaluate using Cross Validation\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "filename = r'C:\\Users\\Hp\\OneDrive\\Documents\\ExcelR\\Python Intro\\Feature Engineering\\pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "num_folds = 10\n",
    "seed = 7\n",
    "kfold = KFold(n_splits=num_folds,)\n",
    "model = LogisticRegression(max_iter=400)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n"
   ]
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    "results.mean()*100.0"
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   "source": [
    "#### 3.Evaluate using Leave One Out Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "# Evaluate using Leave One Out Cross Validation\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "filename = r'C:\\Users\\Hp\\OneDrive\\Documents\\ExcelR\\Python Intro\\Feature Engineering\\pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "loocv = LeaveOneOut()\n",
    "model = LogisticRegression(max_iter=300)\n",
    "results = cross_val_score(model, X, Y, cv=loocv)\n"
   ]
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