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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 5: Hyperparameter Tuning

Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.

Task

Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.

  1. Define a parameter grid to search through. The number of trees should be iterating over the list [10, 20, 30], and the maximum depth of them should be iterating over [5, 10, 20].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

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Section 7. Chapter 5
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bookChallenge 5: Hyperparameter Tuning

Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.

Task

Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.

  1. Define a parameter grid to search through. The number of trees should be iterating over the list [10, 20, 30], and the maximum depth of them should be iterating over [5, 10, 20].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

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 5
toggle bottom row

bookChallenge 5: Hyperparameter Tuning

Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.

Task

Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.

  1. Define a parameter grid to search through. The number of trees should be iterating over the list [10, 20, 30], and the maximum depth of them should be iterating over [5, 10, 20].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

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!

Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.

Task

Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.

  1. Define a parameter grid to search through. The number of trees should be iterating over the list [10, 20, 30], and the maximum depth of them should be iterating over [5, 10, 20].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 7. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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