Page 144 - Artificial Intellegence_v2.0_Class_12
P. 144
. hat do you mean by time series decomposition
Ans. ime series decomposition is a fundamental step in time series analysis because it helps in understanding the
different contributing factors in the data and can aid in making forecasts or predictions. It considers a series as a
combination of level, trend, seasonality and noise components.
. ame the components of the time series decomposition.
Ans. he components of the time series decomposition are as follo s
Level: he average of the series.
Trend: Any increasing or decreasing value in the series.
Seasonality: Any repeating short term cycle in the series.
Noise: Any random variation in the series.
. hat is algorithm and ho does it determine the category for a ne instance
Ans. earest eighbour algorithm is one of the simplest upervised earning based achine earning algorithms.
he algorithm assumes similarity bet een the ne case and the e isting cases and assigns the ne instance
to the category that matches the e isting cases the most closely. his algorithm can be applied to both classification
and regression.
B. Long answer type questions.
. plain the cross validation procedure.
Ans. ross validation is a resampling technique for evaluating machine learning models on a small sample of data.
he process includes only one parameter, k, that specifies the number of groups into hich a given data sample
should be divided. It's a popular strategy since it's straightfor ard to grasp and produces a less biased or optimistic
estimate of model competence than other approaches, such as a simple train test split. he follo ing is the general
procedure
i. andomly shuffle the dataset.
ii. Organise the data into k groups.
iii. or each distinct group
• As a test data set, take a group.
• or training data set, use the remaining groupings.
• it the model to the training set and evaluate it against the test set.
• eep the evaluation score but toss out the model.
iv. sing the sample of model evaluation scores, summarise the model's ability.
. hat is rain est plit valuation tate the reasons for choosing this technique.
Ans. he train test procedure measures the performance of machine learning algorithms hen they need to make
predictions on data that ere not used to train the model. he technique divides the provided dataset into t o
subsets the training dataset and test dataset. he reasons for choosing this technique are
• arge dataset
• o estimate the machine learning model's performance on ne data that as not used to train the model
• etter computational efficiency
• A quick overvie of model performance
. plain the stages of data preparation.
Ans. Stage 4: Data Collection: During the initial data collection phase, data scientists identify available data sources
structured, unstructured, and semi structured) relevant to the problem area. If there is a gap in data collection, the
data scientist may need to modify data requirements accordingly and collect ne and or more data.
Stage 5: Data Understanding: After the initial data collection, techniques such as descriptive statistics and
visualisations can be applied to datasets to evaluate the content, quality, and initial insights of the data. Additional
data collection may be required to fill the gap.
Touchpad Artificial Intelligence (Ver. 2.0)-XII

