How do you think quantum computing will affect machine learning?
With the recent announcement of more breakthroughs in quantum computing, the question of how this new format and way of thinking through hardware serves as a useful proxy to explain classical computing and machine learning, and some of the hardware nuances that might make some algorithms much easier to do on a quantum machine. Demonstrating some knowledge in this area helps show that you’re interested in machine learning at a much higher level than just implementation details.
What are collinearity and multicollinearity?
Collinearity is a linear association between two predictors. Multicollinearity is a situation where two or more predictors are highly linearly related.
What is log likelihood in logistic regression?
It is the sum of the likelihood residuals. At record level, the natural log of the error (residual) is calculated for each record, multiplied by minus one, and those values are totaled. That total is then used as the basis for deviance (2 x ll) and likelihood (exp(ll)).
The same calculation can be applied to a naive model that assumes absolutely no predictive power, and a saturated model assuming perfect predictions.
The likelihood values are used to compare different models, while the deviances (test, naive, and saturated) can be used to determine the predictive power and accuracy. Logistic regression accuracy of the model will always be 100 percent for the development data set, but that is not the case once a model is applied to another data set.
How do you handle the missing or corrupted data in a dataset?
In Python Pandas, there are two methods that are very useful. We can use these two methods to locate the lost or corrupted data and discard those values:
isNull(): For detecting the missing values, we can use the isNull() method.
dropna(): For removing the columns/rows with null values, we can use the dropna() method.
Also, we can use fillna() to fill the void values with a placeholder value.
What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain—it’s a very common way to extract features from audio signals or other time series such as sensor data.
Name a few hyper-parameters of decision trees?
The most important features which one can tune in decision trees are:
- Splitting criteria
- Min_leaves
- Min_samples
- Max_depth
What is the significance of Gamma and Regularization in SVM?
The gamma defines influence. Low values meaning ‘far’ and high values meaning ‘close’. If gamma is too large, the radius of the area of influence of the support vectors only includes the support vector itself and no amount of regularization with C will be able to prevent overfitting. If gamma is very small, the model is too constrained and cannot capture the complexity of the data.
The regularization parameter (lambda) serves as a degree of importance that is given to miss-classifications. This can be used to draw the tradeoff with OverFitting.
How does the SVM algorithm deal with self-learning?
SVM has a learning rate and expansion rate which takes care of this. The learning rate compensates or penalises the hyperplanes for making all the wrong moves and expansion rate deals with finding the maximum separation area between classes.
What evaluation approaches would you work to gauge the effectiveness of a machine learning model?
You would first split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data. You should then implement a choice selection of performance metrics: here is a fairly comprehensive list. You could use measures such as the F1 score, the accuracy, and the confusion matrix. What’s important here is to demonstrate that you understand the nuances of how a model is measured and how to choose the right performance measures for the right situations.
Do you suggest that treating a categorical variable as continuous variable would result in a better predictive model?
For better predictions, categorical variable can be considered as a continuous variable only when the variable is ordinal in nature.