BIDA 630 Data Analytics TEST (Graded A+ actual test)
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BIDA 630 Data Analytics
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BIDA 630 Data Analytics
_____________ of data is used to assess the performance of each supervised learning
model so that we can compare models and pick the best one.
- The test partition
- The validation partition - ️️Validation
The validation partition is used to assess the performance of each supervised learnin...
BIDA 630 Data Analytics
_____________ of data is used to assess the performance of each supervised learning
model so that we can compare models and pick the best one.
- The test partition
- The validation partition - ✔️✔️Validation
The validation partition is used to assess the performance of each supervised learning
model so that we can compare models and pick the best one. In some algorithms (e.g.,
classification and regression trees, k-nearest neighbors) the validation partition may be
used in automated fashion to tune and improve the model. This means that the
validation data are actually used to help build the model.
This is unsupervised learning, if we assume that we do not know what will be purchased
in the future.
The test data are used to build models, or to further tweak the model or improve its fit.
- True
- False - ✔️✔️False
The test data are not used to build models, or to further tweak the model or improve its
fit. (If the test data were used for these purposes, they would play a role in building or
selecting the best model, and would no longer provide an unbiased assessment of the
chosen model's performance with completely new data.)
When a model is fit to training data, zero error with those data is not necessarily good.
This special case is called ______.
- Overestimating
- Good fit
- Overfitting - ✔️✔️Overfitting
Overfitting occurs when the model captures not only the generalizeable pattern in the
data, but also the error. When we split the data into training and validation sets, we
assume that the same pattern (if there is a pattern) exists in both, and that they differ
only in the error that they contain. An absurd and false model may fit perfectly (on
training data set) if the model has enough complexity. Therefore, we may get zero error
for such a model using the training dataset. Such a model, however, is not likely to give
useful results on the validation data set.
, Bar charts are useful for comparing a single statistic (e.g. average, count, percentage)
across groups. The height of the bar represents the value of statistic, and different bars
correspond to different groups.
- True
- False - ✔️✔️True
Which of the following are the most popular visualization tools in JMP_Pro? (3 correct
answers)
- Distribution
- Fit Y by X
- Graph Builder
- Data visualizer
- Graph wizard - ✔️✔️- Distribution
- Fit Y by X
- Graph Builder
Scatter plots play important role in prediction. Next step can be developing a model.
Scatter plots provide information about relationships (linear or non-linear) between
variables. The variables in scatter plot ________.
- can be nominal
- must be numerical
- can be both numerical and categorical
- must be ordinal - ✔️✔️- must be numerical
In a box plot, the box include %50 of the data, the horizontal line represents
(i)____________, the top and bottom of the box represent (ii)________, respectively.
- (i) the mean, (ii) 75th and 25th percentiles
- (i) the mean, (ii) 10th and 90th percentiles
- (i) the median (50th percentile), (ii) bounds for outliers
- (i) the median (50th percentile), (ii) 75th and 25th percentiles - ✔️✔️- (i) the median
(50th percentile), (ii) 75th and 25th percentiles
In JMP a diamond is displayed in the box, where the center of the diamond is
_________.
- The median
- The mean
- The skewness value
- The halfway between outliers - ✔️✔️- The mean
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