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1. • • Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning (ML) process.
     www.techtarget.com › searchenterpriseai › definition

     What is Machine Learning Bias? | Definition from WhatIs

2. Top results related to what is bias in machine learning

   • What is inductive bias in machine learning?

     Top Answer

     Answered Jun 03, 2019 · 91 votes

     Every machine learning algorithm with any ability to generalize beyond the training data that it sees has some type of inductive bias, which are the assumptions made by the model to learn the target function and to generalize beyond training data.

     For example, in linear regression, the model assumes that the output or dependent variable is related to independent variable linearly (in the weights). This is an inductive bias of the model.

     Other Answers

     Answered Jan 23, 2020 · 58 votes

     What is inductive bias?

     Pretty much every design choice in machine learning signifies some sort of inductive bias. "Relational inductive biases, deep learning, and graph networks" (Battaglia et. al, 2018) is an amazing 🙌 read, which I will be referring to throughout this answer.

     An inductive bias allows a learning algorithm to prioritize one solution (or interpretation) over another, independent of the observed data. [...] Inductive biases can express assumptions about either the data-generating process or the space of solutions.

     Examples in deep learning

     Concretely speaking, the very composition of layers 🍰 in deep learning provides a type of relational inductive bias: hierarchical processing. The type of layer imposes further relational inductive biases:

     More generally, non-relational inductive biases used in deep learning include:

     • activation non-linearities,
     • weight decay,
     • dropout,
     • batch and layer normalization,
     • data augmentation,
     • training curricula,
     • optimization algorithms,
     • anything that imposes constraints on the learning trajectory.

     Examples outside of deep learning

     In a Bayesian model, inductive biases are typically expressed through the choice and parameterization of the prior distribution. Adding a Tikhonov regularization penalty to your loss function implies assuming that simpler hypotheses are more likely.

     Conclusion

     The stronger the inductive bias, the better the sample efficiency--this can be understood in terms of the bias-variance tradeoff. Many modern deep learning methods follow an “end-to-end” design philosophy which emphasizes minimal a priori representational and computational assumptions, which explains why they tend to be so data-intensive. On the other hand, there is a lot of research into baking stronger relational inductive biases into deep learning architectures, e.g. with graph networks.

     An aside about the word "inductive"

     In philosophy, inductive reasoning refers to generalization from specific observations to a conclusion. This is a counterpoint to deductive reasoning, which refers to specialization from general ideas to a conclusion.

     Other Answers

     Answered Mar 19, 2019 · 27 votes

     Inductive bias is the set of assumptions a learner uses to predict results given inputs it has not yet encountered.

     Check all answers on Stack Overflow

     1/5

   • What Is Meta-Learning in Machine Learning in R

     In traditional machine learning, models are typically trained on a specific dataset for a specific task, and their performance is optimized for that particular task. However, in R Programming Language the focus is on building models that can leverage prior knowledge or experience to quickly adapt to new tasks with minimal additional training data.

     What is Meta-Learning?Meta-learning, also known as learning to learn, refers to the process of designing algorithms that enable machines to learn how to learn new tasks or domains more efficiently and effectively. In essence, meta-learning aims to develop models or systems that can adapt and generalize from past learning experiences to new, unseen tasks or datasets.

     We implement model-agnostic meta-learning (MAML), in R using the mldr package. In this example, we use a toy regression task for demonstration purposes.

     -# Install and load required packagesinstall.packages("mldr")library(mldr)# Generate synthetic data for meta-learning# Let's create a toy regression task with sine wave datanum_samples <- 100x <- seq(0, 2*pi, length.out = num_samples)y <- sin(x)# Split data into meta-train and meta-test setsmeta train...

     • Split the data into meta-training and meta-testing sets.
     • Define a simple linear regression model as our meta-learner.
     • Implement the MAML algorithm, where we adapt the meta-learner on the meta-training set and evaluate its performance on the meta-test set.
     • Finally we evaluate the performance of the meta-learner on the meta-test set, for example, by calculating the mean squared error.
     • datasets: for accessing the Iris dataset.
     • class: for the K-nearest neighbors (KNN) algorithm.
     • caret: for data splitting.
     • The Iris dataset is split into meta-training and meta-testing sets using the createDataPartition() function from the caret package.
     • 80% of the data is used for meta-training, and the remaining 20% is used for meta-testing.
     • The meta_learner() function is defined to create a meta-learner model.
     • Species column is converted to a factor in both training and testing data.
     • A KNN model with k=3 is fitted using the training data and tested on the testing data.
     • The LearningToLearn() function is defined to perform the meta-learning process.
     • It trains the meta-learner model on the meta-training data.
     • Predictions are made on the meta-testing data using the trained meta-learner.
     • Accuracy is calculated by comparing the predictions with the actual species labels in the meta-testing data.
     • Meta-learning is performed by calling the LearningToLearn() function with the meta-training and meta-testing data.
     • The function returns the accuracy of the meta-learner on the meta-test set.

     Check all answers on Geeksforgeeks

     2/5

   • What is a Learning Curve in machine learning?

     Top Answer

     Answered Jun 25, 2019 · 59 votes

     It usually refers to a plot of the prediction accuracy/error vs. the training set size (i.e: how better does the model get at predicting the target as you the increase number of instances used to train it)

     Usually both the training and test/validation performance are plotted together so we can diagnose the bias-variance tradeoff (i.e determine if we benefit from adding more training data, and assess the model complexity by controlling regularization or number of features).

     Other Answers

     Answered Jan 23, 2020 · 58 votes

     What is inductive bias?

     Pretty much every design choice in machine learning signifies some sort of inductive bias. "Relational inductive biases, deep learning, and graph networks" (Battaglia et. al, 2018) is an amazing 🙌 read, which I will be referring to throughout this answer.

     An inductive bias allows a learning algorithm to prioritize one solution (or interpretation) over another, independent of the observed data. [...] Inductive biases can express assumptions about either the data-generating process or the space of solutions.

     Examples in deep learning

     Concretely speaking, the very composition of layers 🍰 in deep learning provides a type of relational inductive bias: hierarchical processing. The type of layer imposes further relational inductive biases:

     More generally, non-relational inductive biases used in deep learning include:

     • activation non-linearities,
     • weight decay,
     • dropout,
     • batch and layer normalization,
     • data augmentation,
     • training curricula,
     • optimization algorithms,
     • anything that imposes constraints on the learning trajectory.

     Examples outside of deep learning

     In a Bayesian model, inductive biases are typically expressed through the choice and parameterization of the prior distribution. Adding a Tikhonov regularization penalty to your loss function implies assuming that simpler hypotheses are more likely.

     Conclusion

     The stronger the inductive bias, the better the sample efficiency--this can be understood in terms of the bias-variance tradeoff. Many modern deep learning methods follow an “end-to-end” design philosophy which emphasizes minimal a priori representational and computational assumptions, which explains why they tend to be so data-intensive. On the other hand, there is a lot of research into baking stronger relational inductive biases into deep learning architectures, e.g. with graph networks.

     An aside about the word "inductive"

     In philosophy, inductive reasoning refers to generalization from specific observations to a conclusion. This is a counterpoint to deductive reasoning, which refers to specialization from general ideas to a conclusion.

     Other Answers

     Answered Mar 19, 2019 · 27 votes

     Inductive bias is the set of assumptions a learner uses to predict results given inputs it has not yet encountered.

     Check all answers on Stack Overflow

     3/5

   • What is 'fit' in Machine learning?

     Top Answer

     Answered Jul 20, 2020 · 4 votes

     A machine learning model is typically specified with some functional form that includes parameters.

     An example is a line intended to model data that has an outcome variable y that can be described in terms of a feature x. In that case, the functional form would be:

     y = mx + b-

     fitting the model means finding values for m and b that are in accordance with training data, which is a set of points (x1, y1), (x2, y2), ..., (xN, yN). It may not be possible to set m and b such that the line passes through all training data points, but some loss function could be defined for describing a well-fit line. The fitting algorithm's purpose would be to minimize that loss function. In the case of line fitting, the loss could be the total distance of training data points to the line, but it may be more mathematically convenient to set the loss to the total squared distance of training data points to the line.

     In general, a model can be more complex than a line and include many parameters. For some models, the number of parameters is not fixed and can change as part of the fitting process. The features and the outcome variable can be discrete, continuous, and/or multidimensional. For unsupervised problems, there is no outcome variable.

     In all these cases, fitting is still analogous to the line example above, where an algorithm is run to find model parameters that in some sense explain the training data. This often involves running some optimization procedure.

     A model that is well-fit to the training data may not be well-fit to other non-training data, even if the other data is sampled from the same distribution as the training data. A technique called regularization can be used to address this issue.

     Check all answers on Stack Overflow

     4/5

   • What is Standardization in Machine Learning

     In Machine Learning we train our data to predict or classify things in such a manner that isn’t hardcoded in the machine. So for the first, we have the Dataset or the input data to be pre-processed and manipulated for our desired outcomes. Any ML Model to be built follows the following procedure:

     • Collect Data
     • Perform Data Munging/Cleaning (Feature Scaling)
     • Pre-Process Data
     • Apply Visualizations

     Feature Scaling is a method to standardize the features present in the data in a fixed range. It has to perform during the data pre-processing. It has two main ways: Standardization and Normalization.

     StandardizationThe steps to be followed are : 

     Data collectionOur data can be in various formats i.e., numbers (integers) & words (strings), for now, we’ll consider only the numbers in our Dataset.

     Assume our dataset has random numeric values in the range of 1 to 95,000 (in random order). Just for our understanding consider a small Dataset of barely 10 values with numbers in the given range and randomized order.

     1) 992) 7893) 14) 5415) 56) 65897) 941428) 79) 5082610) 35464

     If we just look at these values, their range is so high,...

     • Down Scaling the Values to a scale common to all, usually in the range -1 to +1.
     • And keeping the Range between the values intact.
     • Converts the Mean (μ) to 0
     • Converts to S.D. (σ) to 1
     • Calculating the Z-Score
     • Comparing the Original Values and Standardized Values
     • Comparing the Range of both using Scatter Plots

     Check all answers on Geeksforgeeks

     5/5

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3. People also ask

   What is bias error in machine learning?

   • These differences between actual or expected values and the predicted values are known as error or bias error or error due to bias. Bias is a systematic error that occurs due to wrong assumptions in the machine learning process.

   Bias and Variance in Machine Learning - GeeksforGeeks

   www.geeksforgeeks.org/bias-vs-variance-in-machine-learning/

   See all results for this question

   What is bias in machine learning data sets and models?

   • Bias in machine learning data sets and models is such a problem that you'll find tools from many of the leaders in machine learning development. Detecting bias starts with the data set. A data set might not represent the problem space (such as training an autonomous vehicle with only daytime data).

   Machine learning and bias - IBM Developer

   developer.ibm.com/articles/machine-learning-and-bias/

   See all results for this question

   What is an example of bias in machine learning?

   • One example of bias in machine learning comes from a tool used to assess the sentencing and parole of convicted criminals (COMPAS). Because of overcrowding in many prisons, assessments are sought to identify prisoners who have a low likelihood of re-offending.

   Machine learning and bias - IBM Developer

   developer.ibm.com/articles/machine-learning-and-bias/

   See all results for this question

   What is data bias?

   • Data bias occurs when the training data used to develop a model is not representative of the population or scenario being analyzed. For example, if a facial recognition system is trained on a dataset that only includes white faces, it may have difficulty recognizing faces of other races.

   Understanding Bias in Machine Learning: A Comprehensive Guide

   www.machinelearninghelp.org/tutorials/introduction/what-is-bias-in-machine-learning/

   See all results for this question

4. www.techtarget.com › searchenterpriseai › definitionWhat is Machine Learning Bias? | Definition from WhatIs

   www.techtarget.com › searchenterpriseai › definition

   What is machine learning bias (AI bias)? Machine learning bias, also known as algorithm bias or AI bias , is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning ( ML ) process.

5. www.machinelearninghelp.org › tutorialsUnderstanding Bias in Machine Learning: A Comprehensive Guide

   www.machinelearninghelp.org › tutorials

   Oct 15, 2023 · Bias in machine learning refers to the tendency of a model to consistently make predictions that are influenced by preconceived notions or prejudices, rather than being based on the actual data. Bias can creep into a model through various means, including the data used to train the model, the algorithms and techniques employed, and the ...

6. Videos

   • 

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     Machine Learning Fundamentals: Bias and Variance

     Sep 17, 2018

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     2:33

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     3 types of bias in AI | Machine learning

     Aug 25, 2017

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     18:49

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     6.7. Bias Variance Tradeoff | Machine Learning

     Jul 26, 2021

     14K Views

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7. www.geeksforgeeks.org › bias-vs-variance-inBias and Variance in Machine Learning - GeeksforGeeks

   www.geeksforgeeks.org › bias-vs-variance-in

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   • What Is Bias?
   • What Is Variance?
   • Different Combinations of Bias-Variance
   • Bias Variance Tradeoff
   • GeneratedCaptionsTabForHeroSec

   Bias is simply defined as the inability of the model because of that there is some difference or error occurring between the model’s predicted value and the actual value. These differences between actual or expected values and the predicted values are known as error or bias error or error due to bias. Bias is a systematic error that occurs due to w...

   See full list on geeksforgeeks.org

   Variance is the measure of spread in data from its meanposition. In machine learning variance is the amount by which the performance of a predictive model changes when it is trained on different subsets of the training data. More specifically, variance is the variability of the model that how much it is sensitive to another subset of the training d...

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   There can be four combinations between bias and variance. 1. High Bias, Low Variance:A model with high bias and low variance is said to be underfitting. 2. High Variance, Low Bias: A model with high variance and low bias is said to be overfitting. 3. High-Bias, High-Variance: A model has both high bias and high variance, which means that the model ...

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   If the algorithm is too simple (hypothesis with linear equation) then it may be on high bias and low variance condition and thus is error-prone. If algorithms fit too complex (hypothesis with high degree equation) then it may be on high variance and low bias. In the latter condition, the new entries will not perform well. Well, there is something b...

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   Bias is the inability of the model to fit the data due to wrong assumptions, while variance is the sensitivity of the model to changes in the data. Learn how to reduce bias and variance errors, and their combinations, in machine learning models.

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8. link.springer.com › article › 10A Comprehensive Review of Bias in Deep Learning Models ...

   link.springer.com › article › 10

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   May 8, 2024 · As artificial intelligence and machine learning technologies become increasingly integrated into our lives, understanding and mitigating bias in these systems is of paramount importance. This paper scrutinizes the multifaceted nature of bias, encompassing data bias, algorithmic bias, and societal bias, and explores the interconnectedness among ...

9. developer.ibm.com › articles › machine-learning-and-biasMachine learning and bias - IBM Developer

   developer.ibm.com › articles › machine-learning-and-bias

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   Aug 27, 2019 · Learn how bias is introduced, detected, and eliminated from machine learning models and data sets. Explore examples, types, and tools for fairness in machine learning.

10. www.baeldung.com › cs › machine-learning-biasesBiases in Machine Learning | Baeldung on Computer Science

    www.baeldung.com › cs › machine-learning-biases

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    Mar 18, 2024 · Learn what bias is and how it affects machine learning applications. Explore the different types of bias, sources of bias, and how to identify and correct bias in machine learning algorithms.

11. towardsdatascience.com › understanding-andUnderstanding and Reducing Bias in Machine Learning

    towardsdatascience.com › understanding-and

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    Apr 5, 2019 · Learn how bias affects machine learning algorithms and how to reduce it. Explore the different types of bias, such as productive, unfair and discriminatory, and see how they impact real-world applications like COMPAS.