Podcast
Questions and Answers
A machine learning engineer is training a model to predict customer churn, but the model performs exceptionally well on the training data and poorly on new, unseen data. Which of the following strategies would be most effective to address this issue?
A machine learning engineer is training a model to predict customer churn, but the model performs exceptionally well on the training data and poorly on new, unseen data. Which of the following strategies would be most effective to address this issue?
- Increasing the complexity of the model by adding more layers.
- Simplifying the model and increasing the amount of training data. (correct)
- Ignoring the issue, as high training accuracy always indicates a robust model.
- Reducing the amount of training data to match the test data size.
A data scientist is tasked with grouping customers into distinct segments based on their purchasing behavior, without any prior knowledge of the segments. Which machine learning approach is most suitable for this task?
A data scientist is tasked with grouping customers into distinct segments based on their purchasing behavior, without any prior knowledge of the segments. Which machine learning approach is most suitable for this task?
- Unsupervised learning using a clustering algorithm. (correct)
- Reinforcement learning to optimize future purchases.
- Regression analysis to predict future spending.
- Supervised learning using a classification algorithm.
When using the K-Nearest Neighbors (KNN) algorithm, how does the choice of the 'K' value most significantly impact the model's performance?
When using the K-Nearest Neighbors (KNN) algorithm, how does the choice of the 'K' value most significantly impact the model's performance?
- A larger 'K' can help to smooth out the decision boundaries, reducing the impact of noisy data points, but may mask minority classes. (correct)
- A larger 'K' makes the model more sensitive to noise in the data, potentially overfitting the training set.
- The 'K' value has no significant impact on the model's performance, as KNN is a non-parametric algorithm.
- A smaller 'K' makes the model more robust to outliers but may oversimplify the decision boundary.
Which of the following scenarios is most appropriately addressed using Logistic Regression?
Which of the following scenarios is most appropriately addressed using Logistic Regression?
A machine learning team uses Python's scikit-learn library to build a classification model and needs to assess its performance. Which metric provides the most comprehensive evaluation, especially when dealing with imbalanced datasets?
A machine learning team uses Python's scikit-learn library to build a classification model and needs to assess its performance. Which metric provides the most comprehensive evaluation, especially when dealing with imbalanced datasets?
Flashcards
What is Machine Learning?
What is Machine Learning?
A subset of AI where systems learn from data without explicit programming.
What is Supervised Learning?
What is Supervised Learning?
A type of machine learning where the algorithm learns from labeled data.
What is Logistic Regression?
What is Logistic Regression?
A predictive algorithm used when the dependent variable is categorical.
What is overfitting?
What is overfitting?
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What is Accuracy?
What is Accuracy?
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Study Notes
- Machine Learning is a subset of AI where systems learn from data.
Types of Machine Learning
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
Supervised Learning
- Training data includes both inputs and corresponding outputs.
Algorithms for Classification Tasks
- Logistic Regression
Overfitting
- Model performs well on training data but poorly on new data
Performance Metrics for Classification Models
- Accuracy
K-Nearest Neighbors (KNN)
- K represents the number of nearest neighbors to consider
Common Python Library for ML
- TensorFlow
Algorithms for Clustering
- K-Means
Confusion Matrix
- Used to evaluate classification performance
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