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    "# Scikit-Learn Homework\n",
    "We will work with breast cancer dataset to compare three different classifiers:\n",
    "\n",
    "1. Logistic regression\n",
    "2. Random forest classifier\n",
    "3. K-nearest neighbors\n",
    "\n",
    "Add the appropriate lines of code to answer the questions in each cell.\n",
    "\n",
    "Please use the documentation on the [scikit learn](https://scikit-learn.org/stable/) webpage.\n",
    "\n",
    "You may also consult the example notebook from the lecture [Logistic Regression Classifier](http://rcs.bu.edu/classes/MSSP/sp23/MachineLearning/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17a1caaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce23580e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_breast_cancer(as_frame=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75d4f926",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 1 ###\n",
    "##################\n",
    "# Import the test_train_split function \n",
    "# Split the dataset so that 75% of the dataset is for training and 25% is for testing \n",
    "# Set the random state to 55\n",
    "\n",
    "# Print the value of 5th entry of y_test\n",
    "# Print the of row 3 and column 4 of X_train\n",
    "# Remember that Python uses 0-indexing\n",
    "# 2 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acbb6f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 2 ###\n",
    "##################\n",
    "# Scale the data to have 0 mean and unit variance\n",
    "# Print the value of row 3 and column 4 of X_train\n",
    "# 1 point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bf11145",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 3 ###\n",
    "##################\n",
    "# Use the Scikit-Learn documentation and create 3 classifiers\n",
    "# 1. log_r is LogisticRegression, set the solver parameter to 'liblinear' and max_iter to 10\n",
    "# 2. rnd_forest is RandomForestClassifier, set the n_estimators paramter to 5 and the max_depth to 3\n",
    "# 3. k_near is K-Nearest Neighbor classifier, set the n_neighbors parameter to 4\n",
    "# Set the random_state parameter to 55 in the LogisticRegression and RandomForestClassifer classes\n",
    "# 3 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae42333c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 4 ###\n",
    "##################\n",
    "# Report the accuracy of all three models\n",
    "# Print which model is the most accurate?\n",
    "# 4 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7c3a0cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 5 ###\n",
    "##################\n",
    "# Compute the precision for each model\n",
    "# Print which model(s) has the best precision?\n",
    "# 4 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30d584db",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 6 ###\n",
    "##################\n",
    "# Compute the recall for each model\n",
    "# Print which model(s) has the best recall?\n",
    "# 4 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c56a2612",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 7 ###\n",
    "##################\n",
    "# Plot the confusion matrix for the model with the 2nd- highest accuracy.\n",
    "# Ensure the title contains the accuracy of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1735dad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 8 ###\n",
    "##################\n",
    "# Which model would you pick to predict whether a patient had breast cancer or not?\n",
    "# Why would you choose this model?\n",
    "# 2 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "144bc46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 9 ###\n",
    "##################\n",
    "# Compute and print the F1 score for the model you selected in Question 8.\n",
    "# 2 points "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9735b05e",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################\n",
    "### Question 10###\n",
    "##################\n",
    "# Plot the ROC curve for the model selected in Question 8.\n",
    "# Put the AUC score in the legend.\n",
    "# 3 points"
   ]
  }
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