In an earlier post, we discussed the three sources of errors in AI Artificial Intelligence models such as machine and deep learning: noise, bias and variance. But what is the bias-variance trade-off? This was based on a study that showed for certain machine learning models, by reducing bias on the training set, eventually the variance on the test set would start increasing see diagram below. However, several other studies showed that this is only true in a very narrow context i. With over-parametrized neural nets, the single U-curve dips back downwards for variance resulting in low bias AND variance. A parameter would be the weights and biases that comprise the multiple linear regression equation for a neuron in a neural net e. In this example, there are four parameters W1, W2, W3 and b. An over-parametrized neural net would then be a model that has more parameters than data. So for a voice assistant such as Google Home or Siri or Alexa, if there were one million examples in the training Top Nülüfer Escort Tweet Ankara and 10 million parameters in the model trying to understand the question, then this would be an over-parametrized model i. An under-parametrized neural would be where there is more data than parameters e. But the over-parametrized neural net would be analogous to having your cake and eating it too: you can reduce both bias and variance. What is the Bias-Variance Trade-off. What is the bias-variance tradeoff? Record the key bullets to cover in your blog by looking at what other blogs covered Create an outline based on the bullets specified Flesh out the details What are some titles around bias and variance? What is bias and variance? What is bias in machine learning? What is variance in machine learning? Why is Overfitting called high variance? How do I know if my data is Overfitting? How do you deal with Overfitting and Underfitting? How can machine learning reduce bias and variance? What is bias and variance in ML? Is a high variance good or bad? How do you control variance? Is variance biased? What is variance in ML? Variance is controlled by techniques such as regularization which by ordering overfitting allows for better generalization. Or adding more data. Or changing the neural networks architecture Reduce bias by increasing the epochs iterations. Or bigger network add more layers and or neurons What is regularization? Overfitting occurs because the model is trying to predict even the noise in the signal. It is a process of regression does this by constraining or regularizing the Top Nülüfer Escort Tweet Ankara in the parameters by reducing then to 0. An analogy would be like learning about a country by watching their movies e. The impression becomes that there is a high level of crime and violence in the society. Is it high bias and low variance or the other way. And this optimization process requires a loss function to calculate the model error. It is used to evaluate how well an algorithm models a given dataset. Goal is to get to 0. What other loss function can be used to decompose misclassifications? Quadratic loss function is not appropriate because class labels are not numeric.
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