IB Computer Science Topic A4.2.1 Significance of Data Cleaning Revision Notes (HL)

IB Computer Science Topic A4.2.1 Significance of Data Cleaning Revision Notes (HL) | RevisionDojo

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Machine Learning Lifecycle

Define the Problem: Identify the task and desired outcome.
Gather Data: Collect relevant data from various sources.
Data Preprocessing: Clean and prepare data (handle outliers, missing values, formatting).
Exploratory Data Analysis (EDA): Visualise and understand the patterns and relationships in the data.
Feature Engineering & Selection: Create and select the most relevant features to improve model performance.
Choose a Model: Select a suitable machine learning algorithm.
Split the Data: Divide the data into training and testing (or validation) sets.
Train the Model: Fit the model to the training data.
Model Evaluation: Assess the model’s performance using test/validation data.
Parameter Tuning: Optimise hyperparameters to improve accuracy.
Deployment: Integrate the model into a real-world environment.
Monitor and Maintain: Track performance over time and retrain as needed.

The Significance of Data Cleaning

The Impact of Data Quality on Model Performance

Accuracy and Reliability: High-quality data enables models to make precise predictions. Conversely, poor-quality data leads to inaccurate and unreliable outcomes.
Generalization: Models trained on clean data generalize better to unseen data, performing well in real-world scenarios.
Bias and Fairness: Ensuring data is representative and unbiased prevents models from perpetuating or amplifying biases.

Note

There is an old proverb in computing: "garbage in, garbage out."
This means that the quality of the input data directly impacts the quality of the model's predictions.

Techniques for Data Cleaning

Handling Outliers

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Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

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Machine Learning Lifecycle

Define the Problem: Identify the task and desired outcome.
Gather Data: Collect relevant data from various sources.
Data Preprocessing: Clean and prepare data (handle outliers, missing values, formatting).
Exploratory Data Analysis (EDA): Visualise and understand the patterns and relationships in the data.
Feature Engineering & Selection: Create and select the most relevant features to improve model performance.
Choose a Model: Select a suitable machine learning algorithm.
Split the Data: Divide the data into training and testing (or validation) sets.
Train the Model: Fit the model to the training data.
Model Evaluation: Assess the model’s performance using test/validation data.
Parameter Tuning: Optimise hyperparameters to improve accuracy.
Deployment: Integrate the model into a real-world environment.
Monitor and Maintain: Track performance over time and retrain as needed.

The Significance of Data Cleaning

The Impact of Data Quality on Model Performance

Accuracy and Reliability: High-quality data enables models to make precise predictions. Conversely, poor-quality data leads to inaccurate and unreliable outcomes.
Generalization: Models trained on clean data generalize better to unseen data, performing well in real-world scenarios.
Bias and Fairness: Ensuring data is representative and unbiased prevents models from perpetuating or amplifying biases.

Note

There is an old proverb in computing: "garbage in, garbage out."
This means that the quality of the input data directly impacts the quality of the model's predictions.

Techniques for Data Cleaning

Handling Outliers

Definition

Paywall

(on a website) an arrangement whereby access is restricted to users who have paid to subscribe to the site.

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Duis aute irure dolor in reprehenderit

Note

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam quis nostrud exercitation.

Excepteur sint occaecat cupidatat non proident

Tip

Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

IB Computer Science Topic A4.2.1: Significance of Data Cleaning Notes

A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

Machine Learning Lifecycle

The Significance of Data Cleaning

The Impact of Data Quality on Model Performance

Techniques for Data Cleaning

Handling Outliers

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

Unlock the rest of this chapter with a free account

Machine Learning Lifecycle

The Significance of Data Cleaning

The Impact of Data Quality on Model Performance

Techniques for Data Cleaning

Handling Outliers

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

Unlock the rest of this chapter with a free account