[Data Science Interview,
Questions and
Answers….]
1. What is the difference between supervised and unsupervised machine learning? - ASwers-Supervised
Machine learning:
Supervised machine learning requires training labeled data.
Unsupervised Machine learning:
Unsupervised machine learning doesn't required labeled data.
2. What is bias, variance trade off ? - ASwers-The goal of any supervised machine learning algorithm is to
have low bias and low variance to achive good prediction performance.
The k-nearest neighbors algorithm has low bias and high variance, but the trade-off can be changed by
increasing the value of k which increases the number of neighbors that contribute to the prediction and
in turn increases the bias of the model.
The support vector machine algorithm has low bias and high variance, but the trade-off can be changed
by increasing the C parameter that influences the number of violations of the margin allowed in the
training data which increases the bias but decreases the variance.
There is no escaping the relationship between bias and variance in machine learning.
Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
, bias - ASwers-"Bias is error introduced in your model due to over simplification of machine learning
algorithm." It can lead to underfitting. When you train your model at that time model makes simplified
assumptions to make the target function easier to understand.
Low bias machine learning algorithms - Decision Trees, k-NN and SVM
Hight bias machine learning algorithms - Liear Regression, Logistic Regression
variance - ASwers-"Variance is error introduced in your model due to complex machine learning
algorithm, your model learns noise also from the training dataset and performs bad on test dataset." It
can lead high sensitivity and overfitting.
Normally, as you increase the complexity of your model, you will see a reduction in error due to lower
bias in the model. However, this only happens till a particular point. As you continue to make your
model more complex, you end up over-fitting your model and hence your model will start suffering from
high variance.
3. What is exploding gradients ? - ASwers-"Exploding gradients are a problem where large error
gradients accumulate and result in very large updates to neural network model weights during training."
At an extreme, the values of weights can become so large as to overflow and result in NaN values.
This has the effect of your model being unstable and unable to learn from your training data. Now let's
understand what is the gradient.
Gradient: - ASwers-Gradient is the direction and magnitude calculated during training of a neural
network that is used to update the network weights in the right direction and by the right amount.
4. What is a confusion matrix? - ASwers-The confusion matrix is a 2X2 table that contains 4 outputs
provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity,
precision and recall are derived from it. Confusion Matrix
Basic measures derived from the confusion matrix - ASwers-Error Rate = (FP+FN)/(P+N)
Accuracy = (TP+TN)/(P+N)
Questions and
Answers….]
1. What is the difference between supervised and unsupervised machine learning? - ASwers-Supervised
Machine learning:
Supervised machine learning requires training labeled data.
Unsupervised Machine learning:
Unsupervised machine learning doesn't required labeled data.
2. What is bias, variance trade off ? - ASwers-The goal of any supervised machine learning algorithm is to
have low bias and low variance to achive good prediction performance.
The k-nearest neighbors algorithm has low bias and high variance, but the trade-off can be changed by
increasing the value of k which increases the number of neighbors that contribute to the prediction and
in turn increases the bias of the model.
The support vector machine algorithm has low bias and high variance, but the trade-off can be changed
by increasing the C parameter that influences the number of violations of the margin allowed in the
training data which increases the bias but decreases the variance.
There is no escaping the relationship between bias and variance in machine learning.
Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
, bias - ASwers-"Bias is error introduced in your model due to over simplification of machine learning
algorithm." It can lead to underfitting. When you train your model at that time model makes simplified
assumptions to make the target function easier to understand.
Low bias machine learning algorithms - Decision Trees, k-NN and SVM
Hight bias machine learning algorithms - Liear Regression, Logistic Regression
variance - ASwers-"Variance is error introduced in your model due to complex machine learning
algorithm, your model learns noise also from the training dataset and performs bad on test dataset." It
can lead high sensitivity and overfitting.
Normally, as you increase the complexity of your model, you will see a reduction in error due to lower
bias in the model. However, this only happens till a particular point. As you continue to make your
model more complex, you end up over-fitting your model and hence your model will start suffering from
high variance.
3. What is exploding gradients ? - ASwers-"Exploding gradients are a problem where large error
gradients accumulate and result in very large updates to neural network model weights during training."
At an extreme, the values of weights can become so large as to overflow and result in NaN values.
This has the effect of your model being unstable and unable to learn from your training data. Now let's
understand what is the gradient.
Gradient: - ASwers-Gradient is the direction and magnitude calculated during training of a neural
network that is used to update the network weights in the right direction and by the right amount.
4. What is a confusion matrix? - ASwers-The confusion matrix is a 2X2 table that contains 4 outputs
provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity,
precision and recall are derived from it. Confusion Matrix
Basic measures derived from the confusion matrix - ASwers-Error Rate = (FP+FN)/(P+N)
Accuracy = (TP+TN)/(P+N)
