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Beginners Tutorial on XGBoost and Parameter Tuning in R

Beginners Tutorial on XGBoost and Parameter Tuning in R

Author
Manish Saraswat
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December 20, 2016
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15 min read
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Introduction

Last week, we learned about Random Forest Algorithm. Now we know it helps us reduce a model's variance by building models on resampled data and thereby increases its generalization capability. Good!

Now, you might be wondering, what to do next for increasing a model's prediction accuracy ? After all, an ideal model is one which is good at both generalization and prediction accuracy. This brings us to Boosting Algorithms.

Developed in 1989, the family of boosting algorithms has been improved over the years. In this article, we'll learn about XGBoost algorithm.

XGBoost is the most popular machine learning algorithm these days. Regardless of the data type (regression or classification), it is well known to provide better solutions than other ML algorithms. In fact, since its inception (early 2014), it has become the "true love" of kaggle users to deal with structured data. So, if you are planning to compete on Kaggle, xgboost is one algorithm you need to master.

In this article, you'll learn about core concepts of the XGBoost algorithm. In addition, we'll look into its practical side, i.e., improving the xgboost model using parameter tuning in R.

On 5th March 2017: How to win Machine Learning Competitions ?

Table of Contents

  1. What is XGBoost? Why is it so good?
  2. How does XGBoost work?
  3. Understanding XGBoost Tuning Parameters
  4. Practical - Tuning XGBoost using R

Machine learning challenge, ML challenge

What is XGBoost ? Why is it so good ?

XGBoost (Extreme Gradient Boosting) is an optimized distributed gradient boosting library. Yes, it uses gradient boosting (GBM) framework at core. Yet, does better than GBM framework alone. XGBoost was created by Tianqi Chen, PhD Student, University of Washington. It is used for supervised ML problems. Let's look at what makes it so good:

  1. Parallel Computing: It is enabled with parallel processing (using OpenMP); i.e., when you run xgboost, by default, it would use all the cores of your laptop/machine.
  2. Regularization: I believe this is the biggest advantage of xgboost. GBM has no provision for regularization. Regularization is a technique used to avoid overfitting in linear and tree-based models.
  3. Enabled Cross Validation: In R, we usually use external packages such as caret and mlr to obtain CV results. But, xgboost is enabled with internal CV function (we'll see below).
  4. Missing Values: XGBoost is designed to handle missing values internally. The missing values are treated in such a manner that if there exists any trend in missing values, it is captured by the model.
  5. Flexibility: In addition to regression, classification, and ranking problems, it supports user-defined objective functions also. An objective function is used to measure the performance of the model given a certain set of parameters. Furthermore, it supports user defined evaluation metrics as well.
  6. Availability: Currently, it is available for programming languages such as R, Python, Java, Julia, and Scala.
  7. Save and Reload: XGBoost gives us a feature to save our data matrix and model and reload it later. Suppose, we have a large data set, we can simply save the model and use it in future instead of wasting time redoing the computation.
  8. Tree Pruning: Unlike GBM, where tree pruning stops once a negative loss is encountered, XGBoost grows the tree upto max_depth and then prune backward until the improvement in loss function is below a threshold.

I'm sure now you are excited to master this algorithm. But remember, with great power comes great difficulties too. You might learn to use this algorithm in a few minutes, but optimizing it is a challenge. Don't worry, we shall look into it in following sections.

How does XGBoost work ?

XGBoost belongs to a family of boosting algorithms that convert weak learners into strong learners. A weak learner is one which is slightly better than random guessing. Let's understand boosting first (in general).

Boosting is a sequential process; i.e., trees are grown using the information from a previously grown tree one after the other. This process slowly learns from data and tries to improve its prediction in subsequent iterations. Let's look at a classic classification example:

explain boosting

Four classifiers (in 4 boxes), shown above, are trying hard to classify + and - classes as homogeneously as possible. Let's understand this picture well.

  1. Box 1: The first classifier creates a vertical line (split) at D1. It says anything to the left of D1 is + and anything to the right of D1 is -. However, this classifier misclassifies three + points.
  2. Box 2: The next classifier says don't worry I will correct your mistakes. Therefore, it gives more weight to the three + misclassified points (see bigger size of +) and creates a vertical line at D2. Again it says, anything to right of D2 is - and left is +. Still, it makes mistakes by incorrectly classifying three - points.
  3. Box 3: The next classifier continues to bestow support. Again, it gives more weight to the three - misclassified points and creates a horizontal line at D3. Still, this classifier fails to classify the points (in circle) correctly.
  4. Remember that each of these classifiers has a misclassification error associated with them.
  5. Boxes 1,2, and 3 are weak classifiers. These classifiers will now be used to create a strong classifier Box 4.
  6. Box 4: It is a weighted combination of the weak classifiers. As you can see, it does good job at classifying all the points correctly.

That's the basic idea behind boosting algorithms. The very next model capitalizes on the misclassification/error of previous model and tries to reduce it. Now, let's come to XGBoost.

As we know, XGBoost can used to solve both regression and classification problems. It is enabled with separate methods to solve respective problems. Let's see:

Classification Problems: To solve such problems, it uses booster = gbtree parameter; i.e., a tree is grown one after other and attempts to reduce misclassification rate in subsequent iterations. In this, the next tree is built by giving a higher weight to misclassified points by the previous tree (as explained above).

Regression Problems: To solve such problems, we have two methods: booster = gbtree and booster = gblinear. You already know gbtree. In gblinear, it builds generalized linear model and optimizes it using regularization (L1,L2) and gradient descent. In this, the subsequent models are built on residuals (actual - predicted) generated by previous iterations. Are you wondering what is gradient descent? Understanding gradient descent requires math, however, let me try and explain it in simple words:

  • Gradient Descent: It is a method which comprises a vector of weights (or coefficients) where we calculate their partial derivative with respective to zero. The motive behind calculating their partial derivative is to find the local minima of the loss function (RSS), which is convex in nature. In simple words, gradient descent tries to optimize the loss function by tuning different values of coefficients to minimize the error.
gradient descent convex function

Hopefully, up till now, you have developed a basic intuition around how boosting and xgboost works. Let's proceed to understand its parameters. After all, using xgboost without parameter tuning is like driving a car without changing its gears; you can never up your speed.

Note: In R, xgboost package uses a matrix of input data instead of a data frame.

Understanding XGBoost Tuning Parameters

Every parameter has a significant role to play in the model's performance. Before hypertuning, let's first understand about these parameters and their importance. In this article, I've only explained the most frequently used and tunable parameters. To look at all the parameters, you can refer to its official documentation.

XGBoost parameters can be divided into three categories (as suggested by its authors):
  • General Parameters: Controls the booster type in the model which eventually drives overall functioning
  • Booster Parameters: Controls the performance of the selected booster
  • Learning Task Parameters: Sets and evaluates the learning process of the booster from the given data

  1. General Parameters
    1. Booster[default=gbtree]
      • Sets the booster type (gbtree, gblinear or dart) to use. For classification problems, you can use gbtree, dart. For regression, you can use any.
    2. nthread[default=maximum cores available]
      • Activates parallel computation. Generally, people don't change it as using maximum cores leads to the fastest computation.
    3. silent[default=0]
      • If you set it to 1, your R console will get flooded with running messages. Better not to change it.

  2. Booster Parameters
  3. As mentioned above, parameters for tree and linear boosters are different. Let's understand each one of them:

    Parameters for Tree Booster

    1. nrounds[default=100]
      • It controls the maximum number of iterations. For classification, it is similar to the number of trees to grow.
      • Should be tuned using CV
    2. eta[default=0.3][range: (0,1)]
      • It controls the learning rate, i.e., the rate at which our model learns patterns in data. After every round, it shrinks the feature weights to reach the best optimum.
      • Lower eta leads to slower computation. It must be supported by increase in nrounds.
      • Typically, it lies between 0.01 - 0.3
    3. gamma[default=0][range: (0,Inf)]
      • It controls regularization (or prevents overfitting). The optimal value of gamma depends on the data set and other parameter values.
      • Higher the value, higher the regularization. Regularization means penalizing large coefficients which don't improve the model's performance. default = 0 means no regularization.
      • Tune trick: Start with 0 and check CV error rate. If you see train error >>> test error, bring gamma into action. Higher the gamma, lower the difference in train and test CV. If you have no clue what value to use, use gamma=5 and see the performance. Remember that gamma brings improvement when you want to use shallow (low max_depth) trees.
    4. max_depth[default=6][range: (0,Inf)]
      • It controls the depth of the tree.
      • Larger the depth, more complex the model; higher chances of overfitting. There is no standard value for max_depth. Larger data sets require deep trees to learn the rules from data.
      • Should be tuned using CV
    5. min_child_weight[default=1][range:(0,Inf)]
      • In regression, it refers to the minimum number of instances required in a child node. In classification, if the leaf node has a minimum sum of instance weight (calculated by second order partial derivative) lower than min_child_weight, the tree splitting stops.
      • In simple words, it blocks the potential feature interactions to prevent overfitting. Should be tuned using CV.
    6. subsample[default=1][range: (0,1)]
      • It controls the number of samples (observations) supplied to a tree.
      • Typically, its values lie between (0.5-0.8)
    7. colsample_bytree[default=1][range: (0,1)]
      • It control the number of features (variables) supplied to a tree
      • Typically, its values lie between (0.5,0.9)
    8. lambda[default=0]
      • It controls L2 regularization (equivalent to Ridge regression) on weights. It is used to avoid overfitting.
    9. alpha[default=1]
      • It controls L1 regularization (equivalent to Lasso regression) on weights. In addition to shrinkage, enabling alpha also results in feature selection. Hence, it's more useful on high dimensional data sets.

    Parameters for Linear Booster

    Using linear booster has relatively lesser parameters to tune, hence it computes much faster than gbtree booster.
    1. nrounds[default=100]
      • It controls the maximum number of iterations (steps) required for gradient descent to converge.
      • Should be tuned using CV
    2. lambda[default=0]
      • It enables Ridge Regression. Same as above
    3. alpha[default=1]
      • It enables Lasso Regression. Same as above

  4. Learning Task Parameters
  5. These parameters specify methods for the loss function and model evaluation. In addition to the parameters listed below, you are free to use a customized objective / evaluation function.

    1. Objective[default=reg:linear]
      • reg:linear - for linear regression
      • binary:logistic - logistic regression for binary classification. It returns class probabilities
      • multi:softmax - multiclassification using softmax objective. It returns predicted class labels. It requires setting num_class parameter denoting number of unique prediction classes.
      • multi:softprob - multiclassification using softmax objective. It returns predicted class probabilities.
    2. eval_metric [no default, depends on objective selected]
      • These metrics are used to evaluate a model's accuracy on validation data. For regression, default metric is RMSE. For classification, default metric is error.
      • Available error functions are as follows:
        • mae - Mean Absolute Error (used in regression)
        • Logloss - Negative loglikelihood (used in classification)
        • AUC - Area under curve (used in classification)
        • RMSE - Root mean square error (used in regression)
        • error - Binary classification error rate [#wrong cases/#all cases]
        • mlogloss - multiclass logloss (used in classification)

We've looked at how xgboost works, the significance of each of its tuning parameter, and how it affects the model's performance. Let's bolster our newly acquired knowledge by solving a practical problem in R.

Practical - Tuning XGBoost in R

In this practical section, we'll learn to tune xgboost in two ways: using the xgboost package and MLR package. I don't see the xgboost R package having any inbuilt feature for doing grid/random search. To overcome this bottleneck, we'll use MLR to perform the extensive parametric search and try to obtain optimal accuracy.

I'll use the adult data set from my previous random forest tutorial. This data set poses a classification problem where our job is to predict if the given user will have a salary <=50K or >50K.

Using random forest, we achieved an accuracy of 85.8%. Theoretically, xgboost should be able to surpass random forest's accuracy. Let's see if we can do it. I'll follow the most common but effective steps in parameter tuning:

  1. First, you build the xgboost model using default parameters. You might be surprised to see that default parameters sometimes give impressive accuracy.
  2. If you get a depressing model accuracy, do this: fix eta = 0.1, leave the rest of the parameters at default value, using xgb.cv function get best n_rounds. Now, build a model with these parameters and check the accuracy.
  3. Otherwise, you can perform a grid search on rest of the parameters (max_depth, gamma, subsample, colsample_bytree etc) by fixing eta and nrounds. Note: If using gbtree, don't introduce gamma until you see a significant difference in your train and test error.
  4. Using the best parameters from grid search, tune the regularization parameters(alpha,lambda) if required.
  5. At last, increase/decrease eta and follow the procedure. But remember, excessively lower eta values would allow the model to learn deep interactions in the data and in this process, it might capture noise. So be careful!

This process might sound a bit complicated, but it's quite easy to code in R. Don't worry, I've demonstrated all the steps below. Let's get into actions now and quickly prepare our data for modeling (if you don't understand any line of code, ask me in comments):

# set working directory
path <- "~/December 2016/XGBoost_Tutorial"
setwd(path)

# load libraries
library(data.table)
library(mlr)

# set variable names
setcol <- c("age",
            "workclass",
            "fnlwgt",
            "education",
            "education-num",
            "marital-status",
            "occupation",
            "relationship",
            "race",
            "sex",
            "capital-gain",
            "capital-loss",
            "hours-per-week",
            "native-country",
            "target")

# load data
train <- read.table("adultdata.txt", header = FALSE, sep = ",",
                    col.names = setcol, na.strings = c(" ?"),
                    stringsAsFactors = FALSE)
test <- read.table("adulttest.txt", header = FALSE, sep = ",",
                   col.names = setcol, skip = 1,
                   na.strings = c(" ?"), stringsAsFactors = FALSE)

# convert data frame to data table
setDT(train)
setDT(test)

# check missing values
table(is.na(train))
sapply(train, function(x) sum(is.na(x)) / length(x)) * 100
table(is.na(test))
sapply(test, function(x) sum(is.na(x)) / length(x)) * 100

# quick data cleaning
# remove extra character from target variable
library(stringr)
test[, target := substr(target, start = 1, stop = nchar(target) - 1)]

# remove leading whitespaces
char_col <- colnames(train)[sapply(test, is.character)]
for (i in char_col) set(train, j = i, value = str_trim(train[[i]], side = "left"))
for (i in char_col) set(test, j = i, value = str_trim(test[[i]], side = "left"))

# set all missing value as "Missing"
train[is.na(train)] <- "Missing"
test[is.na(test)] <- "Missing"

Up to this point, we dealt with basic data cleaning and data inconsistencies. To use xgboost package, keep these things in mind:

  1. Convert the categorical variables into numeric using one hot encoding
  2. For classification, if the dependent variable belongs to class factor, convert it to numeric

R's base function model.matrix is quick enough to implement one hot encoding. In the code below, ~.+0 leads to encoding of all categorical variables without producing an intercept. Alternatively, you can use the dummies package to accomplish the same task. Since xgboost package accepts target variable separately, we'll do the encoding keeping this in mind:

# using one hot encoding
>labels <- train$target
>ts_label <- test$target
>new_tr <- model.matrix(~.+0, data = train[,-c("target"), with = FALSE])
>new_ts <- model.matrix(~.+0, data = test[,-c("target"), with = FALSE])

# convert factor to numeric
>labels <- as.numeric(labels) - 1
>ts_label <- as.numeric(ts_label) - 1

For xgboost, we'll use xgb.DMatrix to convert data table into a matrix (most recommended):

# preparing matrix
>dtrain <- xgb.DMatrix(data = new_tr, label = labels)
&t;dtest <- xgb.DMatrix(data = new_ts, label = ts_label)

As mentioned above, we'll first build our model using default parameters, keeping random forest's accuracy 85.8% in mind. I'll capture the default parameters from above (written against every parameter):

# default parameters
params <- list(
    booster = "gbtree",
    objective = "binary:logistic",
    eta = 0.3,
    gamma = 0,
    max_depth = 6,
    min_child_weight = 1,
    subsample = 1,
    colsample_bytree = 1
)

Using the inbuilt xgb.cv function, let's calculate the best nround for this model. In addition, this function also returns CV error, which is an estimate of test error.

xgbcv <- xgb.cv(
    params = params,
    data = dtrain,
    nrounds = 100,
    nfold = 5,
    showsd = TRUE,
    stratified = TRUE,
    print.every.n = 10,
    early.stop.round = 20,
    maximize = FALSE
)
# best iteration = 79

The model returned lowest error at the 79th (nround) iteration. Also, if you noticed the running messages in your console, you would have understood that train and test error are following each other. We'll use this insight in the following code. Now, we'll see our CV error:

min(xgbcv$test.error.mean)
# 0.1263

As compared to my previous random forest model, this CV accuracy (100-12.63)=87.37% looks better already. However, I believe cross-validation accuracy is usually more optimistic than true test accuracy. Let's calculate our test set accuracy and determine if this default model makes sense:

# first default - model training
xgb1 <- xgb.train(
    params = params,
    data = dtrain,
    nrounds = 79,
    watchlist = list(val = dtest, train = dtrain),
    print.every.n = 10,
    early.stop.round = 10,
    maximize = FALSE,
    eval_metric = "error"
)

# model prediction
xgbpred <- predict(xgb1, dtest)
xgbpred <- ifelse(xgbpred > 0.5, 1, 0)

The objective function binary:logistic returns output predictions rather than labels. To convert it, we need to manually use a cutoff value. As seen above, I've used 0.5 as my cutoff value for predictions. We can calculate our model's accuracy using confusionMatrix() function from caret package.

# confusion matrix
library(caret)
confusionMatrix(xgbpred, ts_label)
# Accuracy - 86.54%

# view variable importance plot
mat <- xgb.importance(feature_names = colnames(new_tr), model = xgb1)
xgb.plot.importance(importance_matrix = mat[1:20])  # first 20 variables

xgboost variable importance plot

As you can see, we've achieved better accuracy than a random forest model using default parameters in xgboost. Can we still improve it? Let's proceed to the random / grid search procedure and attempt to find better accuracy. From here on, we'll be using the MLR package for model building. A quick reminder, the MLR package creates its own frame of data, learner as shown below. Also, keep in mind that task functions in mlr doesn't accept character variables. Hence, we need to convert them to factors before creating task:

# convert characters to factors
fact_col <- colnames(train)[sapply(train, is.character)]
for (i in fact_col) set(train, j = i, value = factor(train[[i]]))
for (i in fact_col) set(test, j = i, value = factor(test[[i]]))

# create tasks
traintask <- makeClassifTask(data = train, target = "target")
testtask <- makeClassifTask(data = test, target = "target")

# do one hot encoding
traintask <- createDummyFeatures(obj = traintask, target = "target")
testtask <- createDummyFeatures(obj = testtask, target = "target")

Now, we'll set the learner and fix the number of rounds and eta as discussed above.


#create learner
# create learner
lrn <- makeLearner("classif.xgboost", predict.type = "response")
lrn$par.vals <- list(
    objective = "binary:logistic",
    eval_metric = "error",
    nrounds = 100L,
    eta = 0.1
)

# set parameter space
params <- makeParamSet(
    makeDiscreteParam("booster", values = c("gbtree", "gblinear")),
    makeIntegerParam("max_depth", lower = 3L, upper = 10L),
    makeNumericParam("min_child_weight", lower = 1L, upper = 10L),
    makeNumericParam("subsample", lower = 0.5, upper = 1),
    makeNumericParam("colsample_bytree", lower = 0.5, upper = 1)
)

# set resampling strategy
rdesc <- makeResampleDesc("CV", stratify = TRUE, iters = 5L)

With stratify=T, we'll ensure that distribution of target class is maintained in the resampled data sets. If you've noticed above, in the parameter set, I didn't consider gamma for tuning. Simply because during cross validation, we saw that train and test error are in sync with each other. Had either one of them been dragging or rushing, we could have brought this parameter into action.

Now, we'll set the search optimization strategy. Though, xgboost is fast, instead of grid search, we'll use random search to find the best parameters.

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December 20, 2016
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Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code.

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

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Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

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How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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