Explain the bias-variance tradeoff. Question content area bottom Part 1 Building fully grown trees has no impact on would decrease would increase the variance of the model, making it equally less more reliable in predicting outcomes in a new sample. In general, the more complex the model, the higher lower the bias and the higher lower the variance. Inversely, the less complex the model, the higher lower the bias and the higher lower the variance. Part 2 Data scientists try to balance maximize minimize the bias-variance tradeoff by, for example, always building fully formed trees pruning trees to equal depths pruning trees to various depths . The greater the depth, the higher lower the bias and the higher lower the variance of the model. But at what point is the tradeoff optimal? The company must simply try both models and see which model has a higher overall likelihood value. just one model and ensure that the likelihood values exceed 0.50. just one model and ensure that the likelihood values exceed 0.65. This is the model that optimizes the bias-variance tradeoff