吴恩达Coursera, 机器学习专项课程, Machine Learning：Supervised Machine Learning: Regression and Classification第三

本文主要是介绍吴恩达Coursera, 机器学习专项课程, Machine Learning：Supervised Machine Learning: Regression and Classification第三，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

Practice quiz: Classification with logistic regression

第 1 个问题：Which is an example of a classification task?

【正确】Based on the size of each tumor, determine if each tumor is malignant (cancerous) or not.
Based on a patient's blood pressure, determine how much blood pressure medication (a dosage measured in milligrams) the patient should be prescribed.
Based on a patient's age and blood pressure, determine how much blood pressure medication (measured in milligrams) the patient should be prescribed.
【解释】This task predicts one of two classes, malignant or not malignant.

第 2 个问题：Recall the sigmoid function is g(z) = \frac{1}{1+e^{-z}}，If z is a large positive number, then:

g(z) is near negative one (-1)
g(z) will be near 0.5
g(z) will be near zero (0)
【正确】g(z) is near one (1)

第 3 个问题：A cat photo classification model predicts 1 if it's a cat, and 0 if it's not a cat. For a particular photograph, the logistic regression model outputs g(z)g(z) (a number between 0 and 1). Which of these would be a reasonable criteria to decide whether to predict if it’s a cat?

【正确】Predict it is a cat if g(z) >= 0.5
Predict it is a cat if g(z) < 0.5
Predict it is a cat if g(z) = 0.5
Predict it is a cat if g(z) < 0.7
【解释】Think of g(z) as the probability that the photo is of a cat. When this number is at or above the threshold of 0.5, predict that it is a cat

第 4 个问题：True/False? No matter what features you use (including if you use polynomial features), the decision boundary learned by logistic regression will be a linear decision boundary.

【正确】False
True
【解释】The decision boundary can also be non-linear, as described in the lectures.

Practice quiz: Cost function for logistic regression

第 1 个问题：In this lecture series, "cost" and "loss" have distinct meanings. Which one applies to a single training example?

【正确】Loss
Cost
Both Loss and Cost
Neither Loss nor Cost
【解释】In these lectures, loss is calculated on a single training example. It is worth noting that this definition is not universal. Other lecture series may have a different definition.

第 2 个问题：For the simplified loss function, if the label y(i)=0, then what does this expression simplify to?

Practice quiz: Gradient descent for logistic regression

第1个问题：Which is the correct update step for

The update steps are identical to the update steps for linear regression.
【正确】The update steps look like the update steps for linear regression, but the definition of f_{\vec{w},b}(\mathbf{x}^{(i)})is different.
【解释】For logistic regression, f_{\vec{w},b}(\mathbf{x}^{(i)})is the sigmoid function instead of a straight line.

Practice quiz: The problem of overfitting

第1个问题：Which of the following can address overfitting?

【正确】Collect more training data
【解释】If the model trains on more data, it may generalize better to new examples.
【正确】Apply regularization
【解释】Regularization is used to reduce overfitting.
Remove a random set of training examples
【正确】Select a subset of the more relevant features.
【解释】If the model trains on the more relevant features, and not on the less useful features, it may generalize better to new examples.

第 2 个问题：You fit logistic regression with polynomial features to a dataset, and your model looks like this. What would you conclude? (Pick one)

The model has high variance (overfit). Thus, adding data is, by itself, unlikely to help much.
【正确】The model has high variance (overfit). Thus, adding data is likely to help
The model has high bias (underfit). Thus, adding data is, by itself, unlikely to help much.
The model has high bias (underfit). Thus, adding data is likely to help
【解释】The model has high variance (it overfits the training data). Adding data (more training examples) can help.

第 3 个问题：Suppose you have a regularized linear regression model. If you increase the regularization parameter λ, what do you expect to happen to the parameters w1,w2,...,wn

【正确】This is will reduce the size of the parameters w1,w2,..., wn
This will increase the size of the parameters w1,w2,..., wn
【解释】Regularization reduces overfitting by reducing the size of the parameters w1,w2,...wn

这篇关于吴恩达Coursera, 机器学习专项课程, Machine Learning：Supervised Machine Learning: Regression and Classification第三的文章就介绍到这儿，希望我们推荐的文章对大家有所帮助，也希望大家多多支持为之网！