Flashcards
Machine learning
Flashcards do conjunto (19)
- 1How would you define Machine Learning?Machine Learning is about building systems that can learn from data. Learning
means getting better at some task, given some performance measure. - 2Can you name four types of problems where it shines?Machine Learning is great for complex problems for which we have no algorith-
mic solution, to replace long lists of hand-tuned rules, to build systems that adapt
to fluctuating environments, and finally to help humans learn (e.g., data mining). - 3What is a labeled training set?A labeled training set is a training set that contains the desired solution (a.k.a. a
label) for each instance. - 4What are the two most common supervised tasks?The two most common supervised tasks are regression and classification.
- 5Can you name four common unsupervised tasks?Common unsupervised tasks include clustering, visualization, dimensionality
reduction, and association rule learning. - 6What type of Machine Learning algorithm would you use to allow a robot to walk in various unknown terrains?Reinforcement Learning is likely to perform best if we want a robot to learn to
walk in various unknown terrains since this is typically the type of problem that
Reinforcement Learning tackles. It might be possible to express the problem as a
supervised or semisupervised learning problem, but it would be less natural. - 7What type of algorithm would you use to segment your customers into multiple groups?If you don’t know how to define the groups, then you can use a clustering algo-
rithm (unsupervised learning) to segment your customers into clusters of similar
customers. However, if you know what groups you would like to have, then you
can feed many examples of each group to a classification algorithm (supervised
learning), and it will classify all your customers into these groups. - 8would you frame the problem of spam detection as a supervised learning prob- lem or an unsupervised learning problem?spam detection is a typical supervised learning problem: the algorithm is fed
many emails along with their label (spam or not spam). - 9What is an online learning system?An online learning system can learn incrementally, as opposed to a batch learn-
ing system. This makes it capable of adapting rapidly to both changing data and
autonomous systems, and of training on very large quantities of data. - 10What is out-of-core learning?Out-of-core algorithms can handle vast quantities of data that cannot fit in a
computer’s main memory. An out-of-core learning algorithm chops the data into
mini-batches and uses online learning techniques to learn from these minibatches. - 11What type of learning algorithm relies on a similarity measure to make predic- tions?An instance-based learning system learns the training data by heart; then, when
given a new instance, it uses a similarity measure to find the most similar learned
instances and uses them to make predictions. - 12What is the difference between a model parameter and a learning algorithm’s hyperparameter?A model has one or more model parameters that determine what it will predict
given a new instance (e.g., the slope of a linear model). A learning algorithm tries
to find optimal values for these parameters such that the model generalizes well
to new instances. A hyperparameter is a parameter of the learning algorithm
itself, not of the model (e.g., the amount of regularization to apply). - 13What do model-based learning algorithms search for? What is the most common strategy they use to succeed? How do they make predictions?Model-based learning algorithms search for an optimal value for the model
parameters such that the model will generalize well to new instances. We usually
train such systems by minimizing a cost function that measures how bad the sys-
tem is at making predictions on the training data, plus a penalty for model com-
plexity if the model is regularized. To make predictions, we feed the new
instance’s features into the model’s prediction function, using the parameter val-
ues found by the learning algorithm. - 14Can you name four of the main challenges in Machine Learning?Some of the main challenges in Machine Learning are the lack of data, poor data
quality, nonrepresentative data, uninformative features, excessively simple mod-
els that underfit the training data, and excessively complex models that overfit
the data. - 15If your model performs great on the training data but generalizes poorly to new instances, what is happening? Can you name three possible solutions?If a model performs great on the training data but generalizes poorly to new
instances, the model is likely overfitting the training data (or we got extremely
lucky on the training data). Possible solutions to overfitting are getting more
data, simplifying the model (selecting a simpler algorithm, reducing the number
of parameters or features used, or regularizing the model), or reducing the noise
in the training data. - 16What is a test set and why would you want to use it?A test set is used to estimate the generalization error that a model will make on
new instances, before the model is launched in production. - 17What is the purpose of a validation set?A validation set is used to compare models. It makes it possible to select the best
model and tune the hyperparameters. - 18What can go wrong if you tune hyperparameters using the test set?If you tune hyperparameters using the test set, you risk overfitting the test set,
and the generalization error you measure will be optimistic (you may launch a
model that performs worse than you expect). - 19What is cross-validation and why would you prefer it to a validation set?Cross-validation is a technique that makes it possible to compare models (for
model selection and hyperparameter tuning) without the need for a separate vali-
dation set. This saves precious training data.
Informações
- Descrição
- FlashCards sobre Machine learning, criado por Gabriel Raithz em 11-02-2019.
- Criado por
- Gabriel Raithz
- Visualizações
- 2
- Avaliação