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INDUCTIVE BIAS
Nehru Nagar, Puttur - 574 203, DK, Karnataka, INDIA
Vivekananda College of Engineering & Technology
By
S U TEJAS, 4VP21CD040
06-06-2024 1
06-06-2024 2
CONTENT
• Introduction
• The Learning Process
• Inductive Bias
• Types of Inductive Bias
• Impacts of Inductive Bias
• Examples of Inductive Bias
• Mitigating Inductive Bias
• References
06-06-2024 3
INTRODUCTION
• Machine Learning is the science (and art) of programming computers
so they can learn from data.
• Machine Learning is the field of study that gives computers the
ability to learn without being explicitly programmed. [1]
• A computer program is said to learn from experience E with respect
to some task T and some performance measure P, if its performance
on T, as measured by P, improves with experience E.[2]
06-06-2024 4
THE LEARNING PROCESS:
TRAINING AND GENERALIZATION
• In supervised learning, the algorithm learns from a dataset of labeled
examples.
• The examples consist of inputs (features) and desired outputs (labels).
• The goal is to train the model on this data so it can accurately predict
labels for unseen data (data it wasn't trained on specifically).
• The ability to perform well on unseen data is called generalization.
06-06-2024 5
INDUCTIVE BIAS
• Imagine you're teaching a child a new language. You provide examples
and explanations, but the child also brings their own understanding and
biases based on their prior experiences.
• Machine learning algorithms have inductive bias - a set of assumptions
or preferences built into the algorithm that guide the learning process.
• Inductive bias helps the algorithm focus on what's important in the data
and avoid getting lost in irrelevant details. It steers the learning process
towards specific types of solutions.
06-06-2024 6
TYPES OF INDUCTIVE BIAS
• Prior Knowledge: Some algorithms incorporate prior knowledge about the
problem through their architecture or initial parameters. For example,
convolutional neural networks used for image recognition are inherently biased
towards detecting edges and shapes useful in recognizing objects in images.
• Learning Algorithm Bias: The way an algorithm processes data also introduces
bias. For instance, Support Vector Machines (SVMs) focus on finding the widest
margin between classes. This can be good for clear separation but might miss
subtle patterns in more complex data.
• Occam's Razor Bias: Many machine learning algorithms exhibit a bias towards
simpler explanations. This means they prefer models with fewer parameters to
avoid overfitting, which occurs when the model memorizes the training data too
well and fails to generalize to unseen data.
06-06-2024 7
IMPACTS OF INDUCTIVE BIAS
Positive Impacts:
•Inductive bias helps the algorithm focus on relevant aspects of the data, reducing noise and
irrelevant features.
•It improves efficiency and reduces training time by guiding the learning process towards
productive areas.
•By preventing overfitting, inductive bias encourages generalization to unseen data.
Negative Impacts:
•If the chosen bias doesn't align with the problem, it can lead to biased results. For instance, an
algorithm assuming linearity in data that's actually non-linear might make inaccurate
predictions.
•A strong inductive bias can limit the model's ability to learn complex patterns that don't fit the
bias.
•Inductive bias can make it difficult to interpret model decisions because the bias can influence
the model's reasoning in unseen ways.
06-06-2024 8
• Neural Networks: The architecture of neural networks
introduces inductive bias. Convolutional Neural Networks
(CNNs) with their convolutional layers are inherently biased
towards local features useful in image recognition.
• Decision Trees: The criteria used for splitting data in decision
trees introduce bias. These trees prefer features with high
information gain, which might miss certain relationships if they
don't provide immediate separation between classes.
EXAMPLES OF INDUCTIVE BIAS
06-06-2024 9
• Careful Selection of Learning Algorithms: Choosing an algorithm with an appropriate
inductive bias for the task can help reduce negative impacts. For example, SVMs
might be ideal for classification with clear class separation, but not for complex, non-
linear data.
• Data Augmentation: This technique increases the diversity of training data by creating
variations of existing data (e.g., rotating images for object recognition). This helps
reduce the model's bias towards specific patterns present in the original data.
• Ensemble Methods: Combining multiple models with potentially different biases can
reduce the overall bias and improve generalizability. Averaging predictions from these
models can lead to more robust results.
MITIGATING INDUCTIVE BIAS
06-06-2024 10
REFERENCES
[1] Arthur Samuel, 1959
[2] Tom Mitchell, 1997
06-06-2024 11
THANK YOU

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Understanding Inductive Bias in Machine Learning

  • 1. INDUCTIVE BIAS Nehru Nagar, Puttur - 574 203, DK, Karnataka, INDIA Vivekananda College of Engineering & Technology By S U TEJAS, 4VP21CD040 06-06-2024 1
  • 2. 06-06-2024 2 CONTENT • Introduction • The Learning Process • Inductive Bias • Types of Inductive Bias • Impacts of Inductive Bias • Examples of Inductive Bias • Mitigating Inductive Bias • References
  • 3. 06-06-2024 3 INTRODUCTION • Machine Learning is the science (and art) of programming computers so they can learn from data. • Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. [1] • A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.[2]
  • 4. 06-06-2024 4 THE LEARNING PROCESS: TRAINING AND GENERALIZATION • In supervised learning, the algorithm learns from a dataset of labeled examples. • The examples consist of inputs (features) and desired outputs (labels). • The goal is to train the model on this data so it can accurately predict labels for unseen data (data it wasn't trained on specifically). • The ability to perform well on unseen data is called generalization.
  • 5. 06-06-2024 5 INDUCTIVE BIAS • Imagine you're teaching a child a new language. You provide examples and explanations, but the child also brings their own understanding and biases based on their prior experiences. • Machine learning algorithms have inductive bias - a set of assumptions or preferences built into the algorithm that guide the learning process. • Inductive bias helps the algorithm focus on what's important in the data and avoid getting lost in irrelevant details. It steers the learning process towards specific types of solutions.
  • 6. 06-06-2024 6 TYPES OF INDUCTIVE BIAS • Prior Knowledge: Some algorithms incorporate prior knowledge about the problem through their architecture or initial parameters. For example, convolutional neural networks used for image recognition are inherently biased towards detecting edges and shapes useful in recognizing objects in images. • Learning Algorithm Bias: The way an algorithm processes data also introduces bias. For instance, Support Vector Machines (SVMs) focus on finding the widest margin between classes. This can be good for clear separation but might miss subtle patterns in more complex data. • Occam's Razor Bias: Many machine learning algorithms exhibit a bias towards simpler explanations. This means they prefer models with fewer parameters to avoid overfitting, which occurs when the model memorizes the training data too well and fails to generalize to unseen data.
  • 7. 06-06-2024 7 IMPACTS OF INDUCTIVE BIAS Positive Impacts: •Inductive bias helps the algorithm focus on relevant aspects of the data, reducing noise and irrelevant features. •It improves efficiency and reduces training time by guiding the learning process towards productive areas. •By preventing overfitting, inductive bias encourages generalization to unseen data. Negative Impacts: •If the chosen bias doesn't align with the problem, it can lead to biased results. For instance, an algorithm assuming linearity in data that's actually non-linear might make inaccurate predictions. •A strong inductive bias can limit the model's ability to learn complex patterns that don't fit the bias. •Inductive bias can make it difficult to interpret model decisions because the bias can influence the model's reasoning in unseen ways.
  • 8. 06-06-2024 8 • Neural Networks: The architecture of neural networks introduces inductive bias. Convolutional Neural Networks (CNNs) with their convolutional layers are inherently biased towards local features useful in image recognition. • Decision Trees: The criteria used for splitting data in decision trees introduce bias. These trees prefer features with high information gain, which might miss certain relationships if they don't provide immediate separation between classes. EXAMPLES OF INDUCTIVE BIAS
  • 9. 06-06-2024 9 • Careful Selection of Learning Algorithms: Choosing an algorithm with an appropriate inductive bias for the task can help reduce negative impacts. For example, SVMs might be ideal for classification with clear class separation, but not for complex, non- linear data. • Data Augmentation: This technique increases the diversity of training data by creating variations of existing data (e.g., rotating images for object recognition). This helps reduce the model's bias towards specific patterns present in the original data. • Ensemble Methods: Combining multiple models with potentially different biases can reduce the overall bias and improve generalizability. Averaging predictions from these models can lead to more robust results. MITIGATING INDUCTIVE BIAS
  • 10. 06-06-2024 10 REFERENCES [1] Arthur Samuel, 1959 [2] Tom Mitchell, 1997