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Machine Learning Interview Questions
Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples.
Let’s consider a scenario of a fire emergency :
 
True Positive : If the alarm goes on in case of a fire.
Fire is positive and prediction made by the system is true.

False Positive : If the alarm goes on, and there is no fire.
System predicted fire to be positive which is a wrong prediction, hence the prediction is false.

False Negative : If the alarm does not ring but there was a fire.
System predicted fire to be negative which was false since there was fire.

True Negative : If the alarm does not ring and there was no fire.
The fire is negative and this prediction was true.
Applications of supervised machine learning include :
 
Email Spam Detection : Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.

Healthcare Diagnosis : By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.

Sentiment Analysis : This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment. 

Fraud Detection : By training the model to identify suspicious patterns, we can detect instances of possible fraud.
Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.
 
In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.
Precision is the ratio of correctly predicted positive observation and total predicted positive observation. It shows how precise our model is.

* Precision = TP/TP+FP


Recall is the ratio of the correct predicted positive observation and the total observation in the class.

* Recall = TP/TP+FN


F1-Score is the weighted average of recall and precision.

* F1-Score = 2*(Recall * Precision) / (Recall + Precision)


Accuracy is the ratio of correctly predicted positive observations to the total positive observations.

* Accuracy = TP+TN/TP+TN+FP+FN
Yes, it can be used but it depends on the applications. The predictive models based on machine learning have wide applicability across time series projects. These models help in facilitating the predictive distribution of time and resources. The most widely applied machine learning methods for time series forecasting projects are  : 
 
* Multi-Layer Perceptron (MLP)
* Recurrent Neural Network (RNN)
* Long Short-Term Memory (LSTM)
PCA : Principal Components Analysis
 
KPCA : Kernel-based Principal Component Analysis
 
ICA : Independent Component Analysis
 
These are important feature extraction techniques, which are majorly used for dimensionality reduction.
It is a Receiver Operating Characteristic curve, a fundamental tool for diagnostic test evaluation. ROC curve is a plot of Sensitivity against Specificity for probable cut-off points of a diagnostic test. It is the graphical representation of the contrast between true positive rates and the false positive rate at different thresholds.
Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation that converts a set of correlated variables to a set of uncorrelated variables. PCA is the most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover, PCA is an unsupervised statistical technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.
Kernel trick involves kernel functions which enable higher dimension spaces without actually calculating the coordinates within that dimension. It uses the inner products between the images of all pairs data in a feature space. This allows the calculation of coordinates of higher dimensions with low dimensional data.