[SOLVED] Cs6035 machine learning 2025
5.0
1 customer review
Digital download
Digital download
$25.00
- Availability
- In stock
- Checkout
- One item
Need a hand?
Message us on WhatsApp for payment or download support.
Conceptually, this looks a lot like many if/else statements chained together into a “tree”. A Random Forest expands on this and trains different trees with different subsets of the data and starting conditions. It does this to get a better estimate than a single tree would give. For this project, we are more focused on showing you how to apply these models, so you can simply use the scikit-learn Random Forest model in your code.For this task use scikit-learn’s Random Forest Classifier class and complete the following subtasks:NOTE: Make sure you use the predicted probabilities for roc aucUseful ResourcesINPUTSOUTPUTSFunction Skeletondef calculate_random_forest_metrics(train_features:pd.DataFrame, test_features:pd.DataFrame, train_targets:pd.Series, test_targets:pd.Series, rf_kwargs) -> tuple[ModelMetrics,RandomForestClassifier]: model = RandomForestClassifier() train_metrics = {“accuracy” : 0,“recall” : 0,“precision” : 0,“fscore” : 0,“fpr” : 0,“fnr” : 0,“roc_auc” : 0} test_metrics = {“accuracy” : 0,“recall” : 0,“precision” : 0,“fscore” : 0,“fpr” : 0,“fnr” : 0,“roc_auc” : 0} rf_importance = pd.DataFrame()rf_metrics = ModelMetrics(“Random Forest”,train_metrics,test_metrics,rf_importance) return rf_metrics,modelExample of Feature Importance DataFramecalculate_gradient_boosting_metricsA Gradient Boosted model is more complex than the Naive and Logistic Regression models and similar in structure to the Random Forest model you just trained. A Gradient Boosted model expands on the tree-based model by using its additional trees to predict the errors from the previous tree. For this project, we are more focused on showing you how to apply these models, so you can simply use the scikit-learn Gradient Boosted Model in your code.For this task use scikit-learn’s Gradient Boosting Classifier class and complete the following subtasks:NOTE: Make sure you use the predicted probabilities for roc aucRefer to the Submissions page for details about submitting your work.Useful ResourcesINPUTSOUTPUTSFunction Skeletondef calculate_gradient_boosting_metrics(train_features:pd.DataFrame, test_features:pd.DataFrame, train_targets:pd.Series, test_targets:pd.Series, gb_kwargs) -> tuple[ModelMetrics,GradientBoostingClassifier]:model = GradientBoostingClassifier()train_metrics = {“accuracy” : 0,“recall” : 0,“precision” : 0,“fscore” : 0,“fpr” : 0,“fnr” : 0,“roc_auc” : 0}test_metrics = {“accuracy” : 0,“recall” : 0,“precision” : 0,“fscore” : 0,“fpr” : 0,“fnr” : 0,“roc_auc” : 0} gb_importance = pd.DataFrame()gb_metrics = ModelMetrics(“Gradient Boosting”,train_metrics,test_metrics,gb_importance) return gb_metrics,modelExample of Feature Importance DataFrame Task 5: Model Training and Evaluation:Now that you have written functions for different steps of the model-building process, you will put it all together. You will write code that trains a model with hyperparameters you determine (you should do any tuning locally or in a notebook, i.e., don’t tune your model in gradescope since the autograder will likely timeout).Important: Conduct hyperparameter tuning locally or in a separate notebook. Avoid tuning within Gradescope to prevent autograder timeouts.Develop your own local tests to ensure your code functions correctly before submitting to Gradescope. Do not share these tests with other students.train_model_return_scores (ClaMP Dataset)Instructions (10 points):This function focuses on training a model using the ClaMP dataset and evaluating its performance on a test set.Sample Submission (ClaMP):Function Skeleton (ClaMP):import pandas as pd def train_model_return_scores(train_df, test_df) -> pd.DataFrame:“””Trains a model on the ClaMP training data and returns predicted probabilitiesfor the test data. Args:train_df (pd.DataFrame): ClaMP training data with ‘class’ column.test_df (pd.DataFrame): ClaMP test data without ‘class’ column. Returns:pd.DataFrame: DataFrame with ‘index’ and ‘malware_score’ columns.“””# TODO: Implement the model training and prediction logic as described above.test_scores = pd.DataFrame() # Replace with your implementationreturn test_scorestrain_model_unsw_return_scores (UNSW-NB15 Dataset)Instructions (10 points):This function is similar to the previous one but uses the UNSW-NB15 dataset.Sample Submission (UNSW-NB15):Function Skeleton (UNSW-NB15):import pandas as pd def train_model_unsw_return_scores(train_df, test_df) -> pd.DataFrame:“””Trains a model on the UNSW-NB15 training data and returns predictedprobabilities for the test data. Args:train_df (pd.DataFrame): UNSW-NB15 training data with ‘class’ column.test_df (pd.DataFrame): UNSW-NB15 test data without ‘class’ column. Returns:pd.DataFrame: DataFrame with ‘index’ and ‘prob_class_1’ columns.“””# TODO: Implement the model training and prediction logic as described above.test_scores = pd.DataFrame() # Replace with your implementationreturn test_scoresDeliverablesDataset InformationClaMP DatasetUNSW-NB15 Dataset