Tag: variance

Understanding the Bias-Variance Trade-off

George Box once said, “All models are wrong, but some are useful.” From a supervised machine learning perspective, all models have errors, and to make our models useful, we have to minimize such errors. More specifically, we have to minimize two major sources of error: bias and variance. Prior to applying a machine learning algorithm,…
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Trade off between bias and variance

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. By: Sachin Narang

Bias vs Variance – Much like raising a child

The two errors that are critical to understand model prediction are: Bias and Variance. These concepts can be applied to nearly all sorts of learning in our life. For example, the higher-level understanding of these two concepts can be done through the analogy of raising(training) a young child. Let, Home = Training set Instill best…
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Bias Variance and Tradeoff

Meaning of Bias and Variance? Bias: how unfair is something towards other Variance: how likely something changes with respect to others Thing to remember? High Bias: under fitting High Variance: over fitting What is High Bias and High Variance? Assume there are two examiners determining weather forecast. Where it rains only when it is humid…
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The Bias and Variance tradeoff

Developing the machine learning models isn’t so hard nowadays, due a lot of frameworks and libraries that give build-in functions to easily implement some fancy model. However, it’s important to understand the theory behind all practical implementation in order to build more accurate models. So in this topic will be discussed the theoretical understanding of…
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