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Virtualization software like VMware and VirtualBox provide an easy way for you to install operating systems in a virtual environment. If you are familiar with VMWare, you will know that you need to specify the maximum disk space while creating the virtual machine. This allows VMware to allot the amount of disk space required for your virtual machine. However, you can easily run out of space if you didn’t allocate enough disk space initially. Here is how you can increase the disk space of a virtual machine in VMWare.
Before doing anything, make sure that you have a good backup of your virtual machine as there is a good chance of something going wrong while expanding the virtual disk file. Moreover, I also assume that you know how to use regular partition management tools.
Note: this tutorial is for VMWare. If you want to do the same for VirtualBox, here is the tutorial to resize disk space in VirtualBox.Increase Disk Space from Command Line
To increase virtual disk space in VMware from the command line, the first thing you need to do is to shut down or power off the virtual machine (don’t suspend it) and also make sure that there are no snapshots. If you have any previous snapshots, delete them using the VMware snapshot manager.
Now open the Windows command prompt by pressing “Win + R” and type cmd.
Navigate to the VMware installation folder. You can generally use the below command to do that on 64-bit systems. Do change the file path if you have installed VMware in another folder.
Now to increase the disk size, execute the below command. Don’t forget to change the disk space (in Gb’s) and the location of the virtual disk file as required. If you have multiple “vmdk” files, then use the file that does not include -flat or -s0 in the file name.
Once you have executed the command, VMware will increase the virtual disk size. The increased disk space will appear as unallocated space in your guest operating system. Use the built-in partition management tool to either extend the system partition or to create a new partition for your files and folders.Increase Disk Space using VMware GUI
Note: though I’m showing this in the VMware Workstation, the same steps are applicable to VMware Player.
With the above action, VMware resizes the disk as required and displays a confirmation message letting you know the same.
Now log in to your guest operating system and use the partition management tool to manage the new unallocated space.
That’s all there is to do, and it is that easy to increase virtual disk size in VMware through command line or the graphical user interface.
Vamsi is a tech and WordPress geek who enjoys writing how-to guides and messing with his computer and software in general. When not writing for MTE, he writes for he shares tips, tricks, and lifehacks on his own blog Stugon.
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While creating a partition on a hard disk by using the Diskpart utility, some users encountered the error “There is not enough usable space for this operation.” According to the error message, you are unable to create a new partition because of the lack of usable space on your hard disk. In this article, we will see how to deal with this error to get the issue fixed.
The complete error message displayed by the Diskpart utility could be either of the two:
There is not enough usable space on specified disks to extend the volume
There is not enough usable space for this operationDiskpart Virtual Disk Service error, There is not enough usable space
If Diskpart is showing the “Virtual Disk Service error: There is not enough usable space for this operation” while creating a new partition, the following solutions may help you fix the problem:
Check the partition style of your Disk
Rescan your disk
Shrink the partition on your hard drive
Use a third-party tool1] Check the partition style of your disk
One possible cause of this error is the limitation of an MBR disk. If the disk installed on your system uses the Master Boot Record, you will not create more than 4 partitions on your disk. Whereas, in the GPT partition, there is no such limit. You can create unlimited partitions on a GPT disk, but Windows allows only up to 128 partitions on a GPT disk.
Check the partition style of your hard disk. To do so, follow the steps below:
Launch the Run command box.
The above command will open the Disk Management tool.
Select the Volumes tab. You will see the partition style of your hard disk.
If the partition style of your hard disk is MBR and you already have 4 partitions on it, you have to convert it into GPT to create more partitions. Do note that the conversion is possible only if the hard disk contains no partitions or volumes. If your hard disk contains any partitions, you have to delete them before converting MBR into GPT. This action will also erase all the data on your hard disk. That’s why we suggest you backup all your data on an external hard disk.
The GPT disks use Unified Extensible Firmware Interface (UEFI) BIOS mode. You can check your BIOS mode in the System Information. If your BIOS supports both legacy and UEFI modes but it is set to the legacy mode, you can convert it to UEFI.
After converting your hard disk from MBR to GPT, check if the Diskpart is showing the same error while creating partitions.2] Rescan your disk
The Rescan command helps you detect new disks added to your computer. When you run this command, it detects the configuration changes on your hard disk and updates the information. You can try running this command in Diskpart and see if it helps. Follow the steps below:
Open Command Prompt as an administrator.
Type Diskpart and press Enter.
Type Rescan and press Enter.
After rescanning your disks, check if the issue persists.3] Shrink the partition on your hard drive
According to the error, your disk does not have enough usable space. To fix this problem, you can shrink a partition to leave unallocated space. After shrinking the partition, you should be able to create a new partition. The steps to do this are written below:
Open Command Prompt as an administrator.
Type select disk #. Replace # with the disk number. For example, if you want to create a partition on disk 0, you have to type select disk 0 and press Enter.
Now, you have to select the partition on your hard disk which you want to shrink. For this, type list par and press Enter. This command will list all the partitions on your hard disk.
Now, type sel par #. Replace # with the partition number on your hard disk. After selecting a particular partition, say, partition 3, you will see a message “Partition 3 is now the selected partition.”
Type shrink desired=size minimum=size. After that hit Enter. Replace the string size in the command with the actual size on the disk that you want to shrink. The size that you enter to shrink the volume should be less than the total size of the selected partition. For example, if the total size of the selected partition is 16 GB, the size to shrink the partition should be less than 16 GB.
After the above command is executed successfully, a partition with unallocated space will be created. You can view this unallocated space in Disk Management.
To make the unallocated space usable, you have to format it. Type fs=ntfs quick and press Enter.
Wait till the formatting process gets completed.
After the formatting gets completed, a partition of the desired size is created. In step 6 above, we have used the command shrink desired=size minimum=size. This command consists of two values, namely, desired and minimum. Let’s see the meaning and purpose of each of these values.
Desired: This represents the actual amount of space that you want on your partition.
Minimum: It represents the minimum space on the selected partition.
If the size entered in the desired value is not possible to create, Windows will create the partition of the size that you enter in the minimum value. You can also enter the same size in both of these values.
Read: Diskpart error, The device does not recognize the command.4] Use a third-party tool
You can also use a third-party tool to create partitions on your hard drive. If you search online, you will find many disk and partition manager tools that come with both free and paid plans.
Related:How do you fix the virtual disk service error the specified disk is not convertible?
You may see an error message “The specified disk is not convertible” while converting an MBR disk into GPT by using the Diskpart utility. The complete error message is:
The specified disk is not convertible.
According to the error message, the disk you want to convert from MBR into GPT is not convertible. To fix this error, you should know what an MBR partition style is and what a GPT partition style is. If you want to convert an MBR disc into a GPT, your BIOS should support the UEFI mode. This is because the GPT disks use Unified Extensible Firmware Interface (UEFI) BIOS mode.
If your BIOS supports both legacy and UEFI modes, you will be able to convert legacy to UEFI. If your BIOS mode is already set to UEFI, but you are still getting this error message, you can use a third-party tool or software, like Minitool partition wizard to convert MBR to GPT.
Read: DiskPart error, Data error Cyclic redundancy check.How do I fix the virtual disk service error volume size is too big?
The virtual disk service error volume size is too big usually occurs when you format a particular disk drive by using a FAT32 file system. In such a case, you can try another file system to format the disk drive, like NTFS. There are several limitations of the FAT32 file system. The maximum file size limit for the FAT32 file system is 4 GB. This is the reason why Diskpart displays this error while formatting a disk drive of large size by using the FAT32 file system.
Read next: Diskpart has encountered an error, The media is write-protected.
VMware has released new versions of its desktop virtualization products for the Mac and PC that bring support for Windows 10 and an enhanced graphics engine.
The company’s Fusion line of software that lets Mac users manage and run virtual machines has been updated to version 8, while the Workstation line of Windows virtualization applications has been updated to version 12. The refreshes come right after the launch of Windows 10 last month, and unsurprisingly bring support for running Microsoft’s new operating system in a virtual machine. VMWare
Microsoft PowerMap running in a Windows 7 virtual machine through Workstation Pro 12. PowerMap requires DirectX 10, which is newly supported in VMWare’s latest desktop virtualization products.
One of the key upgrades added to VMware’s PC and Mac virtualization software is an improved graphics engine that brings support for Microsoft’s DirectX 10 and OpenGL 3.3 and improves performance for graphics tasks that the previous versions of the software were able to handle.
In addition, both the Fusion and Workstation lines bring support for up to 16 virtual CPUs, 64GB of RAM and 2GB of video RAM per virtual machine. It’s a change that will let owners of high-powered hardware bring more of it to bear when running virtual machines. Users with encrypted virtual machines will find that they’re faster to suspend and resume with the new updates, which should be a boon to security-conscious folks.
Windows 10 running in a virtual machine on Workstation Pro 12
VMware also took the opportunity to change the names of its PC desktop virtualization products with this update. Workstation has now been renamed to Workstation Pro, while Player Pro is now Workstation Player. The change is supposed to make it easier for people to understand the offerings.
As the name implies, Workstation Pro is meant for power users like IT professionals and developers, while Workstation Player is designed for deploying managed virtual machines to end users in an enterprise.
Cortana running on the OS X desktop through a Fusion 8-powered Windows 10 virtual machine
On the Mac side of things, Fusion and Fusion Pro 8 will let users summon Microsoft’s voice assistant Cortana from the Mac desktop when they have a Windows 10 virtual machine running. The update also supports running on OS X El Capitan, along with virtualizing Apple’s forthcoming operating system.
Like past versions of the software, users can run Windows apps alongside Mac apps using Fusion’s Unity feature, and this upgrade also adds a refined installer so that it’s easier for people to set up new virtual machines. Fusion Pro 8 includes features tailored for power users, developers and IT professionals, like support for IPv6 so they can test how the new networking standard works before rolling it out across a company.
People who bought the immediate predecessors to the updated applications on or after July 29 will get a free upgrade to the new versions. Parallels users interested in switching to Fusion can get 40 percent off the purchase price of a new version of either Fusion 8 or Fusion Pro 8.
A new copy of Fusion Pro 8 will cost US$200, while its consumer-oriented sibling costs $80. Workstation 12 Pro costs $250, and Workstation Player is $150. Users of the two most recent versions of VMWare’s desktop virtualization software, including Fusion 6 and Workstation 10, will get discounts on upgrading to a new version of the software.
On top of all the desktop pricing, people who purchase Fusion Pro 8 or Workstation Pro 12 will get $600 in free credits for the company’s vCloud Air cloud platform or six months of free vCloud Air service, whichever runs out first. It’s twice what VMware offers new users of its cloud platform, and could draw in new customers for the company’s cloud business at a very competitive time in the cloud market overall.
Introduction to VMware
Web development, programming languages, Software testing & others
This software helps us in various domains like security, storage, networking, etc. VMware provides us with various software and products that can be used for different benefits; here, we will see the various benefits of using that product and software for better understanding and usage.Various VMware Benefits
As we already know, VMware has many benefits, which can be understood by the various product it provides, which adds great help to security networking, storage, and many more areas.1. Provides virtual desktop infrastructure
One of the benefits of using this is we can use the desktop from anywhere we want. From this, we do not require a full desktop setup in the workplace; we can use VMware Horizon, which allows us to manage and run the Windows desktop from VMware Cloud or AWS. This removes a lot of things for us, like we do not require to manage and set up the full desktop at the workplace. Also, it helps reduce the monitoring and managing of user security and centralizes management. We can use this with two more VMware products, Dynamic Environment Manager and App Volumes, which help us in application delivery and managing the Windows desktop.2. Provide personal desktop
VMware created this as their first product, enabling users to run or manage virtual machines directly on a single Linux, Windows, or laptop. Using this, we can have a virtual machine inside the physical machine, which can run without causing any issues; in short, it can run parallel or simultaneously. If we talk about virtual machines, they have operating systems such as Linux or Windows. With this, we can even run Windows on the Linux machine and vice versa without worrying about the installed operating system on the machine. The product name VM Workstation enables us to run the virtual machine in the machine; for Mac computers, we have VM Fusion.3. Provide storage and availability 4. Provide disaster recovery management
VMware benefits also include disaster recovery; for this, it provides us with the Site Recovery Manager, which helps us create the recovery plan, which will be executed automatically in the case of failure. The NSX further integrates with this system to maintain and preserve the security and network on the migrated VMs.5. Provide the cloud infrastructure
For infrastructure, we have one product from VMware which is known as vSphere, which provide the following points:
vCenter Server6. Provide us SDDC platform
SDDC manager helps to integrate various software into a single platform, such as VMware NSX, vSphere, vSAN, etc. So for this, we have VMware cloud foundation, which is a software that helps to bundle this mentioned software by the use of the SDDC platform; now we can deploy this bundle on the private cloud or also have the option to run this bundle within as public cloud but as a service. Admin can do all these tasks; admin also has the provision to the application without the need for storage and network.7. Provide network and security
As seen above are the main benefits of VM, as we have already seen it provides us with many products which can be used for different purposes as per the need, one of the main things about doing things virtually without carrying the setup at the workplace.
Below are the key points that need to be kept in mind while using the VN product; they provide us with many benefits, but we also have some drawbacks that must be focused on.
Also, there is a lack of support, which means we may encounter several bugs while using the VM product.
Not all things are free; the fees are very high for licensing.Conclusion – VMware Benefits
As we have already seen so many benefits of VM in this article, we have also seen the different products that provide for different purposes; you can understand and start using them by the above explanation; we have many more things in VM.Recommended Articles
This is a guide to VMware Benefits. Here we discuss the introduction and various VMware benefits for better understanding. You may also have a look at the following articles to learn more –
Enter Gnome Boxes, a program created to make virtualization easier. Does it have what it takes to beat out programs like Virtualbox or VMware? Find out below!Where to get Gnome Boxes
For Ubuntu users:
For Fedora users:
Is your distribution not listed above? Don’t worry. Gnome Boxes is probably in your Linux distros’ software repository. Just open up your package manager and search for “gnome-boxes” or something similar and then install it.
If you are using Gnome Shell, chances are it is already installed in your system.How to set up a virtual machine in Gnome Boxes
Setting up virtual machines in Boxes is fairly simple. The first thing you’ll need to do is obtain an ISO file. The great thing about this program is that the installation process is very hands-off. Just find an ISO (in this example we’ll be using Fedora 22) and paste the direct link inside the “enter URL” box.
Note: it’s also possible to browse for an ISO instead of pasting a URL into the URL box.
After that, Gnome Boxes will go out and obtain an ISO to use. This may take a bit of time.
When the program has finished obtaining the live disc for installation, it will automatically set the ideal settings for the virtual machine you’re looking to create. Don’t bother customizing it unless you’re an expert on virtual machines.
Press the Create button, and your new virtual machine will be created and go to first boot.
Now that your machine has booted up, just go through the standard installation for whatever Linux distribution you’ve selected. When the installation has been completed, reboot your VM.
Once rebooted you’ll be able to use your virtual machine.
Note: you’ll need to eject the live disc after installing it to use your virtual machine.Why you should consider using Gnome Boxes
Boxes aims to make virtualization simple on the Linux platform. For the most part, it succeeds. The program makes it very easy to get a VM going and set up using the very same technology (KVM) that professionals have been using for years now but without the hassle.
Not into servers? Don’t worry. If you’re just looking for a program on Linux that you can run your copy of Windows 7 in, Boxes is perfect. Seeing as how the main goal of this program is its ease of use and simplicity, getting an OS running is a piece of cake.
It’s safe to say, if you’re new to virtualization or maybe you’re just looking to get a machine up and running right away as easily as possible, Gnome Boxes is certainly a good option. It’ll help you get done what you need to. It might not come with the most features, but sometimes that’s exactly what you want.Why you should consider avoiding Gnome Boxes
Boxes is a great tool for beginners and casual virtual machine users looking to accomplish tasks without much fuss, but make no mistake, it’s just the replacement for something like VMware, Virtualbox, Xenserver or something else along those lines.
The biggest problem is that Boxes is centered around simplicity; it’s a watered down front end to a very powerful set of tools. A lot of features you’ve come to expect will most likely be missing. For example: the ability to control and set CPUs, shared folders, video memory or even turning your VM into a headless server will most likely be absent.Conclusion
Boxes is a great tool and a step in the right direction for making virtualization easier to implement. It’s not the fanciest program on the block, but a solid start nonetheless. Those looking to set up a simple virtual machine on their Linux install, be it server-based or a desktop operating system, should seriously give this program a shot.
What do you think about Gnome Boxes? Do you like it? Do you hate it? Let us know below!
Derrik Diener is a freelance technology blogger.
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This article was published as a part of the Data Science Blogathon
But, what if we are able to answer the business questions without the complex metrics and technical jargon? well, we might stand a chance to get a buy-in from them. In this blog, we will take a look at a use case where we will still build our models but explain it in a different way – the business way.Approach to Extract Business Value using ML model
In this blog, we will explore the use of deciles, understand various evaluation plots like Cumulative Gain plots and Lift plots, etc to assess the business value of ML models. The approach would help us explain the predictive power of our ML models and also makes it simple enough to interpret the model outcome. The plots and metrics would enable the business to make informed decisions with a lot more confidence.
We will explore the below topics as we go along in this blog.
Generating deciles and reports
We will be using the publicly available bank dataset from UCI Machine learning Repository There are four datasets in the zip file but our interest is in the bank-additional-full.csv. All the attributes information can be found in the above URL. The data is from the direct marketing phone calls made to contact the client to assess if the client is interested to subscribe to bank term deposits. It would be Yes if subscribed and No if not. Our interest in this blog is to understand how to evaluate the business value of the ML model/models.Data Loading & Processing:
Let us load the data and take a look to get a better understanding.import wget import zipfile import pandas as pd import numpy as np wget.download(url) zf = zipfile.ZipFile('bank-additional.zip') df= pd.read_csv(zf.open('bank-additional/bank-additional-full.csv'), sep=';')
We can carry out complete EDA/feature engineering/selection/select significant variables and then build models but to keep it simple, we will select few variables for model building.df= df[['y', 'duration', 'campaign', 'pdays', 'previous', 'euribor3m']]
Also, let’s explore data a bit more, convert the target variable to categorical and encode it.df.y[df.y == 'yes'] = 'term deposit' df.y = pd.Categorical(df.y) df['y'] = df.y.cat.codes df.info() Output:
RangeIndex: 41188 entries, 0 to 41187
Data columns (total 6 columns):
# Column Non-Null Count Dtype
— —— ————– —–
0 y 41188 non-null int8
1 duration 41188 non-null int64
2 campaign 41188 non-null int64
3 pdays 41188 non-null int64
4 previous 41188 non-null int64
5 euribor3m 41188 non-null float64
dtypes: float64(1), int64(4), int8(1)
memory usage: 1.6 MBdf.head() y duration campaign pdays previous euribor3m 0 261 1 999 0 4.857 0 149 1 999 0 4.857 0 226 1 999 0 4.857 0 151 1 999 0 4.857 0 307 1 999 0 4.857 df.describe() y duration campaign pdays previous euribor3m count 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 41188.000000 mean 0.112654 258.285010 2.567593 962.475454 0.172963 3.621291 std 0.316173 259.279249 2.770014 186.910907 0.494901 1.734447 min 0.000000 0.000000 1.000000 0.000000 0.000000 0.634000 25% 0.000000 102.000000 1.000000 999.000000 0.000000 1.344000 50% 0.000000 180.000000 2.000000 999.000000 0.000000 4.857000 75% 0.000000 319.000000 3.000000 999.000000 0.000000 4.961000 max 1.000000 4918.000000 56.000000 999.000000 7.000000 5.045000 Model Building to Extract Business Value
Step1: Define independent and target variablesy = df.y X = df.drop('y', axis = 1)
Step2: Split the dataset into train/test with a test size of 0.2X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 2023)
Step3: Building logistic regression modelfrom sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # Logistic regression model clf_glm = LogisticRegression(multi_class = 'multinomial', solver = 'newton-cg').fit(X_train, y_train) prob_glm = clf_glm.predict_proba(X_test) max_prob_glm = round(pd.DataFrame(np.amax(prob_glm, axis=1), columns = ['prob_glm']),2)
So, we have built the model and also scored (predicted) it on the test data which gives us predicted probabilities for each of the observations.Building Deciles
The deciles simply put are splitting our data into ten different bins. So, we take all our predicted probabilities, segregate them into ten groups, and rank them meaning that the highest predicted probabilities will be in decile 1 and the lowest setting will be in decile 10. We will use the pandas’s cut() function to split the data.
The below line of code creates a new column by the name Decile_rank_glm which holds the rank of each predicted record.max_prob_glm['Decile_rank_glm'] = pd.cut(max_prob_glm['prob_glm'], 10, labels = np.arange(10,0, -1)) prob_glm Decile_rank_glm 0 0.99 1 1 0.59 9 2 0.96 1 3 0.83 4 4 0.85 4 ... ... ... 8233 0.98 1 8234 0.98 1 8235 0.99 1 8236 0.99 1 8237 0.93 2
Note: The probability of 0.99 is ranked 1, 0.93 is 2, 0.85 is 4, and 0.59 is 9 in the above decile ranks. We will see the visual representation of this result in the later sections.Model Evaluation to Extract Business Value
Any model that we build will have to be compared with the baseline model to see if how the model fairs in its performance. Let us explore this further below.
Random Model: The baseline model will be a random model meaning it is as good as the flip of a coin meaning there is 50% probability that the call to a customer will be positive / customer buys our product. Our logistic regression model’s performance should obviously be better than this.
Wizard Model: This is the other extreme model which is perfect in its prediction meaning it predicts nearly with 100% accuracy. This model should never be used in production or for any business decision as there is a heavy chance of overfitting.
Logistic Model: Our model should be somewhere in between these two extreme models which give us enough confidence to make our business decisions.
We will visualize the above models in a cumulative gain plot. This will give us an indication of where the logistic model stands in terms of performance.kds.metrics.plot_cumulative_gain(y_test.to_numpy(), prob_glm[:,1])
Looks good so far, the plot is on the expected lines and the logistic regression model is in between the two extreme models we have discussed.
Insights from the cumulative gain plot:
If we can select only the top 20% (decile 1 and decile 2) then we have coverage of nearly 80% of the target class.
As this is a cumulative plot, we see that the curve flattens after decile 5 which means the deciles 6 to 10 either have minimal records or none.
The wizard model hits the 100% mark in decile 2 – we already know this is an idealistic model just for reference. In case our model starts nearing/resembling any of these two extreme models then we need to review our model.
We have so far discussed models, deciles, and their performance comparison. Let us explore this further at the decile level to get a better understanding of what is at play here and how we can explain the process better. We will carry out our analysis with help of visuals which makes it much easier. The kds package has a very nice function to generate all the metrics reports in one line of code.kds.metrics.report(y_test, prob_glm[:,1])
Let us understand each of these plots. Please note that the x-axis of all the plots is Deciles.
Lift Plot: This plot shows us how much better is logistic regression model is compared to the random model at all. Eg: decile 2 gives us a lift almost 4 times meaning we can do 4 times better than the random model approach. As we go to higher deciles the lift drops and eventually meets the random model line, this is because all the higher probability score values are in the top deciles (1 to 3) which we had already seen in the cumulative gains plot too. So, bottom deciles will have probabilities that are lower and almost the same as the random model.
Decile-wise Lift Plot: This plot shows us the percentage of the target class observation in each of the deciles and we observe that decile 1 had maximum and as we go higher deciles the percentage drops and after a certain point it even goes below random model line. This is because the random model has equally distributed observations that are randomly set whereas our model has predicted fewer observations in the higher deciles.
Cumulative Gain Plot: We discussed this in the earlier section and also looked into the interpretation of the plot.
KS Statistic Plot: The KS plot evaluates different distributions i.e events and non-events and the KS value is a point where the difference is maximum between the distributions. In short, it helps us in understanding the ability of the ML model to differentiate between two events. The KS score is greater than 40 and if it happens to be in the top 3 deciles then it is considered to be good. In our case, we have a score of 68.932 and decile 3 from the plot.
Let us build one more model with a random forest and see how the results will be.clf_rf = RandomForestClassifier().fit(X_train, y_train) prob_rf = clf_rf.predict_proba(X_test) max_prob_rf = pd.DataFrame(np.amax(prob_rf, axis=1), columns = ['prob_rf']) max_prob_rf['Decile_rank_rf'] = pd.cut(max_prob_rf['prob_rf'], 10, labels = np.arange(10,0, -1)) kds.metrics.plot_cumulative_gain(y_test.to_numpy(), prob_rf[:,1]) kds.metrics.report(y_test, prob_rf[:,1])
The random forest model is slightly better than the logistic model.
Decile 2 gives marginally higher lift and KS statistics is 72.18 compared to logistic which had 68.93Business scenarios
Control Over Recommendations: There are situations where the client has a business demand that a minimum of X number of recommendations should always be generated. In such cases, we can have larger recommendations by considering the top 3 deciles instead of 2 deciles and also have granular control on additional records.
Measure Market Response: The post recommendation analysis and market response are easy to measure. For instance, from the previous point, we can separately track how was the performance of all additional recommendations from decile 3. Did additional push from decile 3 generate any impact (positive or negative)?
Optimizing Marketing Spend: By focusing on the top 20-30%, businesses can save time, resources, and money that they would spend on non-responders or targeting the wrong customers.Closing Note
Technology has its place and businesses have their say. At the end of the day, It is all about the business value that technology brings. It will always be more effective when these gains are explained in business terms. It not only helps in gaining confidence from the business but also opens up new opportunities to explore.
Please note that we built two classification models but didn’t look into the ROC curve, confusion matrix, precision, recall, and other standard metrics that we generally do for such models. It is highly recommended that these metrics are tracked and measured to assess the model’s performance and then follow the decile approach from this blog. Depending on the target audience and the goal, use the pitch that best suits the objective.
Hope you liked the blog. Happy learnings !!!!
You can connect with me – Linkedin
You can find the code on Github
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