mlessentials/Lab08/Examples/tuning_using_randomizedsearchcv.ipynb

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    "from sklearn import datasets, model_selection, linear_model\n",
    "\n",
    "# load the data\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "# target\n",
    "y = diabetes.target\n",
    "\n",
    "# features\n",
    "X = diabetes.data\n",
    "\n",
    "# initialise the ridge regression\n",
    "reg = linear_model.Ridge()"
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    "from scipy import stats\n",
    "\n",
    "# alpha ~ gamma(1,1)\n",
    "param_dist = {'alpha': stats.gamma(a=1, loc=1, scale=2)}"
   ]
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    "# set up the random search to sample 100 values and score on negative mean squared error\n",
    "rscv = model_selection.RandomizedSearchCV(estimator=reg, param_distributions=param_dist, n_iter=100, scoring='neg_mean_squared_error')\n",
    "\n",
    "# start the search\n",
    "rscv.fit(X,y)"
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   "source": [
    "import pandas as pd\n",
    "\n",
    "# convert the results dictionary to a pandas data frame\n",
    "results = pd.DataFrame(rscv.cv_results_)\n",
    "\n",
    "# show the top 5 hyperparamaterizations\n",
    "print(results.loc[:,['params','rank_test_score']].sort_values('rank_test_score').head(5))"
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