train case in pointyour neural network-based -learning agent required episodes to solve frozenlake adding second layer to the neural network agent quadruples the number of necessary training episodes with increasingly complex neural networksthis divide only grows to maintain the same error rateincreasing model complexity increases the sample complexity exponentially likewisedecreasing sample complexity decreases model complexity thuswe cannot maximize model complexity and minimize sample complexity to our heart' desire we canhoweverleverage our knowledge of this tradeoff for visual interpretation of the mathematics behind the bias-variance decompositionsee understanding the bias-variance tradeoff at high levelthe bias-variance decomposition is breakdown of "true errorinto two componentsbias and variance we refer to "true erroras mean squared error (mse)which is the expected difference between our predicted labels and the true labels the following is plot showing the change of "true erroras model complexity increases