Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge 4: Cross-validation | Scikit-learn
Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

bookChallenge 4: Cross-validation

Cross-validation is a pivotal technique in machine learning that aims to assess the generalization performance of a model on unseen data. Given the inherent risk of overfitting a model to a particular dataset cross-validation offers a solution. By partitioning the original dataset into multiple subsets, the model is trained on some of these subsets and tested on the others.

By rotating the testing fold and averaging the results across all iterations, we gain a more robust estimate of the model's performance. This iterative process not only provides insights into the model's potential variability and bias but also aids in mitigating overfitting, ensuring that the model has a balanced performance across different subsets of the data.

Task

Implement a pipeline that combines data preprocessing and model training. After establishing the pipeline, utilize cross-validation to assess the performance of a classifier on the Wine dataset.

  1. Create a pipeline that includes standard scaling and decision tree classifier.
  2. Apply 5-fold cross-validation on the pipeline.
  3. Calculate the average accuracy across all folds.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 7. Chapter 4
toggle bottom row

bookChallenge 4: Cross-validation

Cross-validation is a pivotal technique in machine learning that aims to assess the generalization performance of a model on unseen data. Given the inherent risk of overfitting a model to a particular dataset cross-validation offers a solution. By partitioning the original dataset into multiple subsets, the model is trained on some of these subsets and tested on the others.

By rotating the testing fold and averaging the results across all iterations, we gain a more robust estimate of the model's performance. This iterative process not only provides insights into the model's potential variability and bias but also aids in mitigating overfitting, ensuring that the model has a balanced performance across different subsets of the data.

Task

Implement a pipeline that combines data preprocessing and model training. After establishing the pipeline, utilize cross-validation to assess the performance of a classifier on the Wine dataset.

  1. Create a pipeline that includes standard scaling and decision tree classifier.
  2. Apply 5-fold cross-validation on the pipeline.
  3. Calculate the average accuracy across all folds.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 7. Chapter 4
toggle bottom row

bookChallenge 4: Cross-validation

Cross-validation is a pivotal technique in machine learning that aims to assess the generalization performance of a model on unseen data. Given the inherent risk of overfitting a model to a particular dataset cross-validation offers a solution. By partitioning the original dataset into multiple subsets, the model is trained on some of these subsets and tested on the others.

By rotating the testing fold and averaging the results across all iterations, we gain a more robust estimate of the model's performance. This iterative process not only provides insights into the model's potential variability and bias but also aids in mitigating overfitting, ensuring that the model has a balanced performance across different subsets of the data.

Task

Implement a pipeline that combines data preprocessing and model training. After establishing the pipeline, utilize cross-validation to assess the performance of a classifier on the Wine dataset.

  1. Create a pipeline that includes standard scaling and decision tree classifier.
  2. Apply 5-fold cross-validation on the pipeline.
  3. Calculate the average accuracy across all folds.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Cross-validation is a pivotal technique in machine learning that aims to assess the generalization performance of a model on unseen data. Given the inherent risk of overfitting a model to a particular dataset cross-validation offers a solution. By partitioning the original dataset into multiple subsets, the model is trained on some of these subsets and tested on the others.

By rotating the testing fold and averaging the results across all iterations, we gain a more robust estimate of the model's performance. This iterative process not only provides insights into the model's potential variability and bias but also aids in mitigating overfitting, ensuring that the model has a balanced performance across different subsets of the data.

Task

Implement a pipeline that combines data preprocessing and model training. After establishing the pipeline, utilize cross-validation to assess the performance of a classifier on the Wine dataset.

  1. Create a pipeline that includes standard scaling and decision tree classifier.
  2. Apply 5-fold cross-validation on the pipeline.
  3. Calculate the average accuracy across all folds.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 7. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
some-alt