26 votes
Accepted

What is the meaning of term Variance in Machine Learning Model?

It is pretty much what you said. Formally you can say: Variance, in the context of Machine Learning, is a type of error that occurs due to a model's sensitivity to small fluctuations in the training ...
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  • 7,498
23 votes

Why underfitting is called high bias and overfitting is called high variance?

How can one understand it intuitively? Underfitting is called "Simplifying assumption" (Model is HIGHLY BIASED towards its assumption). your model will think linear hyperplane is good enough to ...
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13 votes
Accepted

RL Advantage function why A = Q-V instead of A=V-Q?

In my understanding, $V(s)$ is always larger than $Q(s,a)$, because the function $V$ includes the reward for the current state $s$, unlike $Q$ This is incorrect. There is not really such a thing as "...
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  • 27.3k
10 votes
Accepted

Question on bias-variance tradeoff and means of optimization

There are a lot of ways bias and variance can be minimized and despite the popular saying it isn't always a tradeoff. The two main reasons for high bias are insufficient model capacity and ...
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  • 7,498
10 votes
Accepted

What are bias and variance in machine learning?

What are Bias and Variance? Let's start with some basic definitions: Bias: it's the difference between average predictions and true values. Variance: it's the variability of our predictions, i.e. how ...
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  • 5,687
6 votes

Elimination of features based on high covariance without affecting performance?

$|d| \gg 0$ means there is a very strong correlation between $x_1$ and $x_2$. This means one can be expressed (almost completely) in terms of the other, thus one of two is almost redundant. A simple ...
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  • 2,063
5 votes

What is the meaning of term Variance in Machine Learning Model?

Variance is the change in prediction accuracy of ML model between training data and test data. Simply what it means is that if a ML model is predicting with an accuracy of "x" on training data and ...
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  • 256
5 votes

Why underfitting is called high bias and overfitting is called high variance?

Let us assume our model to be described by $y = f(x) +\epsilon$, with $E[\epsilon]=0, \sigma_{\epsilon}\neq 0$. Let furthermore $\hat{f}(x)$ be our regression function, i.e. the function whose ...
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  • 536
5 votes
Accepted

Why is there a trade-off between bias and variance in supervised learning? Why can't we have best of both worlds?

The tradeoff between bias and variance summarizes the "tug of war" game between fitting a model that predicts the underlying training dataset well (low bias) and producing a model that doesn't change ...
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  • 1,815
5 votes

Bagging vs Boosting, Bias vs Variance, Depth of trees

Question 1: Bagging (Random Forest) is just an improvement on Decision Tree; Decision Tree has lot of nice properties, but it suffers from overfitting (high variance), by taking samples and ...
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  • 886
4 votes
Accepted

Trade off between Bias and Variance

You want to decide this based on how well your model performs and generalizes. If your model is underfitting, you want to increase your model's complexity, increasing variance and decreasing bias. If ...
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4 votes
Accepted

How to estimate the variance of regressors in scikit-learn?

I believe it is the probabilistic nature of a model that allows you to get the variance of predictions, or more generally defined as the uncertainty of predictions, like the Gaussian process you ...
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  • 3,966
4 votes

Overfitting Naive Bayes

Let me try to answer your questions point by point. Perhaps you already solved your problem, but your questions are interesting and so perhaps other people can benefit from this discussion. Is Naive ...
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4 votes
Accepted

Why can decision trees have a high amount of variance?

It is relatively simple if you understand what variance refers to in this context. A model has high variance if it is very sensitive to (small) changes in the training data. A decision tree has high ...
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  • 5,977
4 votes
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Does variance and standard deviation both measure how spread out the numbers are?

Yes it is. Standard deviation is a square root of variance. Square root is a monotonic transformation, meaning that it preserves the order, e.g, if a > b then <...
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  • 886
4 votes

How does C have effects on bias and variance of a Support Vector Machine?

The C being a regularized parameter, controls how much you want to punish your model for each misclassified point for a given curve. If you put large value to C it will try to reduce errors but at the ...
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  • 1,271
3 votes

Bias-variance tradeoff in practice (CNN)

Normally, the training loss is lower than the validation one. This does not indicate any overfitting. Indeed, it is even suspicious when you training loss is higher than the validation loss. From ...
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3 votes
Accepted

How do you set sigma for the Gaussian similarity kernel?

Updated Answer According to a reference paper in Spectral Clustering (von Luxburg) the $\sigma$ is simply set to 1. A further tuning can be applied with some visualization inspection but I did not ...
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3 votes

Linear machine learning algorithms "often" have high bias/low variance?

The "often" is the key here - the way that linear models are built, especially compared to other types of models, are more likely to favor certain types of errors.... in this case, they are more ...
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  • 31
3 votes

How can I calculate mean and variance incrementally?

This problem was discussed, with proof and some alternate methods over on math.stackexchange.
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3 votes
Accepted

What does high variance mean in a binary classification machine learning model?

You have correctly intuited that variance isn't as useful a concept in this case. Statisticians typically look at the binomial deviance instead (see here for a thorough technical development). If you ...
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3 votes
Accepted

Bagging vs Boosting, Bias vs Variance, Depth of trees

why we are supposed to use weak learners for boosting (high bias) whereas we have to use deep trees for bagging (very high variance) Clearly it wouldn't make sense to bag a bunch of shallow trees/...
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  • 5,977
3 votes

Bagging vs pasting in ensemble learning

Let's say we have a set of 40 numbers from 1 to 40. We have to pick 4 subsets of 10 numbers. Case 1 - Bagging - We will pick the first number, put it back, and then pick the next. This makes all the ...
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  • 5,164
3 votes
Accepted

Which between random forest or extra tree is best in a unbalance dataset?

Both Random Forest Classifier and Extra Trees randomly sample the features at each split point, but because Random Forest is greedy it will try to find the optimal split point at each node whereas ...
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  • 354
2 votes

How can I calculate mean and variance incrementally?

Following that link about moving variance in my comment, I came upon this: Welford's online algorithm for calculating variance, which seems to supply what I'm looking for. Here's the algorithm: <...
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2 votes

How to decide what threshold to use for removing low-variance features?

You can do this a few ways, which I can list in ascending order of effort: Pick a value that seems ok for you and your dataset by eye-balling it then simply cut variables below the theshold from the ...
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  • 13.9k
2 votes
Accepted

How is the equation for the relation between prediction error, bias, and variance defined?

If: $$Err(x)=E[(Y-\hat{f}(x))^2]$$ Then, by adding and substracting $f(x)$, $$Err(x)=E[(Y-f(x)+f(x)-\hat{f}(x))^2] $$ $$= E[(Y-f(x))^2] + E[(\hat{f}(x)-f(x))^2] + 2E[(Y-f(x))(\hat{f}(x)-f(x))]$$ The ...
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  • 5,644
2 votes

Meaning of variance in machine learning models

What is variance? Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. Model with high variance pays a lot of attention to training ...
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  • 136
2 votes

Meaning of variance in machine learning models

Variance actually measures the variability of the model prediction (say, for simplification, for a particular sample instance) if we would retrain the model multiple times (on different subsets of the ...
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