Answer:
The various reasons that could be a major problem for the implementation are it involves a large number of parameters also, having a noisy data
Explanation:
Solution
The various reasons that could be causing the problem is given as follows :
1. A wide number of parameters :
-
In the ensemble tree method, the number of parameters which are needed to be trained is very large in numbers.
- When the training is performed in this tree, then the model files the data too well.
- When the model has tested against the new data point form the validation set, then this causes a large error because the model is trained completely according to the training data.
2. Noisy Data:
- The data used to train the model is taken from the real world . The real world's data set is often noisy i.e. contains the missing filed or the wrong values.
- When the tree is trained on this noisy data, then it sets its parameters according to the training data.
- As regards to testing the model by applying the validate set, the model gives a large error of high in accuracy
y.
A patent grants the right to sell an invention and claim it as your own
Answer:
1.T 2.f 3.t 4.f 5.f 6.f 7.t 8.t 9.f
I don’t think we should because we all have our one choices that we should achieved and we should show
Answer:
beacon frame
Explanation:
Beacon frame is a management frame In computer networks, known to be in IEEE 802.11 based WLANs. These frames are transmitted periodically and they contain all the information a station will require before it can rightly transmit a frame.
When it comes to announcing the presence of devices in a wireless computer network (WLAN), Beacon frames are used, and they can also be used in the synchronization of the devices and services
