Project Cycle- Modelling Flashcards
what sort of an approach does modelling take?
(or)
modelling often is related to a mathematical approach. why?
through data exploration we are able to express the various parameters of data through visual representation.The graphical representation makes
the data understandable for humans as we can discover trends and patterns out of it. But when it
comes to machines accessing and analysing data, it needs the data in the most basic form of numbers
(which is binary – 0s and 1s) and when it comes to discovering patterns and trendsin data, the machine
goes in for mathematical representations of the same. The ability to mathematically describe the
relationship between parameters is the heart of every AI model. Thus, whenever we talk about
developing AI models, it is the mathematical approach towards analysing data which we refer to.
what are the two learning approaches in modelling
-rule based approach
-machine learning approach
describe rule based approach
Refers to the AI modelling where the rules are defined by the developer. The machine follows the
rules or instructions mentioned by the developer and performs its task accordingly.This is known as a rule-based approach because we fed the data along
with rules to the machine and the machine after getting trained on them is now able to predict
answers for the same. So in this approach, the data set with labels and the rules are fed into the machine, where the machine reacts accordignly and gives desired output.
what is the drawback of rule based approahc
A drawback/feature for this approach is that the learning is static. The machine
once trained, does not take into consideration any changes made in the original training dataset. That
is, if you try testing the machine on a dataset which is different from the rules and data you fed it at
the training stage, the machine will fail and will not learn from its mistake. Once trained, the model
cannot improvise itself on the basis of feedbacks.
describe the learning based aproahc
Refers to the AI modelling where the machine learns by itself. Under the Learning Based approach,
the AI model gets trained on the data fed to it and then is able to design a model which is adaptive to
the change in data. That is, if the model is trained with X type of data and the machine designs the
algorithm around it, the model would modify itself according to the changes which occur in the data. In learnign based approach, the data along with the desired output is fed into the machine, while the machine develops its own algorithm to match the data to the output.
machine learning approach is dynamic. substantiate
After training, the machine is now fed with testing data. Now, the
testing data might not have similar data as the ones on which the model has been trained. So, the
model adapts to the features on which it has been trained and accordingly predicts the answers. In this way, the machine learns by itself by adapting to the new data which is
flowing in. This is the machine learning approach which introduces the dynamicity in the model.
what is supervised learning
In a supervised learning model, the dataset
which is fed to the machine is labelled. In
other words, we can say that the dataset is
known to the person who is training the
machine only then he/she is able to label the
data.
what is classification b
classifiction is a type of supervised learning model where the data is classified into groups according to their labels.
The model works on discerete data set and not on continuous data.
the algorithm is able to determine where a particular data point belongs by using the classification function.
what is regression
It is a type of supervised learning model in which the algorithm generates a mapping function from the given data.With these we are able to predict future data
-it workd on continous data.
what is unsupervised learning
An unsupervised learning model works on unlabelled dataset. This means that the data which is fed
to the machine is random and there is a possibility that the person who is training the model does not
have any information regarding it. The unsupervised learning models are used to identify
relationships, patterns and trends out of the data which is fed into it. It helps the user in understanding
what the data is about and what are the major features identified by the machine in it.
what is clustering
clustering refers to the unsupervised learning algotihm which can cluster the unknown data according to the trends or patterns indentified out of it. These patterns observed may be known to the developer or it may be somehing unique pattern.
-clustering works on discerete data
what is dimensionality reductoion
it is an unsupervised learning to handle the real world complex problems when the dimensions of the data are reduced.We humans are able to visualise upto 3-Dimensions only but according to
a lot of theories and algorithms, there are various entities which exist beyond 3-Dimensions. For
example, in Natural language Processing, the words are considered to be N-Dimensional entities.
Which means that we cannot visualise them as they exist beyond our visualisation ability. Hence, to
make sense out of it, we need to reduce their dimensions. Here, dimensionality reduction algorithm
is used.As we reduce the dimension of an entity, the information which it contains starts getting distorted.Hence, to reduce the dimensions and still be able to make sense out of the data, we use Dimensionality
Reduction.
define reinforcement learning
In reinofrcement learing, an agent interacts with its environement by producing actiosn and discovers errors or rewards
describe reinforcement learning
machine learning algorithms help software agents and machines determine the ideal behaviour within a particular context to enhance their working. There is no labelled data set or any results attached to the data. So the only way to perform a task is through learning from experience.
For every right actions or decision, a positive reinforcement is awarded. For every wrong action or decision, a negative reinofrcemnet is given. This way it identifies what actiosn need to be performed and what actions should not be performed
define the evaluation project cycle stage
it is the last stage of the project cyle through which we understand the reliability of the ai model using various measuers
